EEG_reduced
Catherine Walsh
9/21/2020
library(tidyverse)## ── Attaching packages ──────────────────────────────────────────────────────────────────────────────────────── tidyverse 1.3.0 ──## ✓ ggplot2 3.3.2 ✓ purrr 0.3.4
## ✓ tibble 3.0.3 ✓ dplyr 1.0.1
## ✓ tidyr 1.1.1 ✓ stringr 1.4.0
## ✓ readr 1.3.1 ✓ forcats 0.5.0## ── Conflicts ─────────────────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
## x dplyr::filter() masks stats::filter()
## x dplyr::lag() masks stats::lag()library(psych)##
## Attaching package: 'psych'## The following objects are masked from 'package:ggplot2':
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## %+%, alphalibrary(patchwork)
library(rmatio)
library(reshape2)##
## Attaching package: 'reshape2'## The following object is masked from 'package:tidyr':
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## smithslibrary(kableExtra)##
## Attaching package: 'kableExtra'## The following object is masked from 'package:dplyr':
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## group_rowsload("data/behav.RData")
load("data/load_effects_DFR.RData")
load("data/split_groups_info.RData")
source("helper_fxns/load_EEG_data.R")
source("helper_fxns/corr_spectrum_to_indiv_diff.R")
source("helper_fxns/mutate_for_heatmap.R")
source("helper_fxns/split_ERPs_into_groups.R")
source("helper_fxns/create_TC_for_plot.R")
source("helper_fxns/split_spectrum.R")
source("helper_fxns/paired_freq_plot.R")
source("helper_fxns/select_period_average.R")
source("helper_fxns/split_into_groups.R")
source("helper_fxns/prep_split_for_bar_plots.R")
source("helper_fxns/average_electrodes.R")
source("helper_fxns/average_electrodes_spectrum.R")
source("helper_fxns/plot_tertiles.R")
source("helper_fxns/find_inverted_U_spectra.R")
source("helper_fxns/time_sig_load_effects.R")temp <- read.mat("data/EEG/DFR/ERPS_cl15_midOccip_reformatted.mat")
ERPS_times_DFR <- temp[["all_times"]]
temp <- read.mat("data/EEG/DFR/ERSPS_cl15_midOccip_reformatted.mat")
ERSPS_times_DFR <- temp[["all_times"]]
ERSPS_freqs <- temp[["all_freqs"]]
ERPS_midOccip_DFR <- load_EEG_data("DFR", "ERPS_cl15_midOccip")
ERSPS_midOccip_DFR <- load_EEG_data("DFR", "ERSPS_cl15_midOccip")
ERSPS_Oz_DFR <- load_EEG_data("DFR", "ERSPS_Oz")
ERPS_Pz_DFR <- load_EEG_data("DFR", "ERPS_Pz")
ERPS_P1_DFR <- load_EEG_data("DFR", "ERPS_P1")
ERPS_P2_DFR <- load_EEG_data("DFR", "ERPS_P2")
ERPS_CP1_DFR <- load_EEG_data("DFR", "ERPS_CP1")
ERPS_CP2_DFR <- load_EEG_data("DFR", "ERPS_CP2")
ERPS_POz_DFR <- load_EEG_data("DFR", "ERPS_POz")
ERSPS_Fz_DFR <- load_EEG_data("DFR", "ERSPS_Fz")
ERSPS_F1_DFR <- load_EEG_data("DFR", "ERSPS_F1")
ERSPS_F2_DFR <- load_EEG_data("DFR", "ERSPS_F2")
ERSPS_FC1_DFR <- load_EEG_data("DFR", "ERSPS_FC1")
ERSPS_FC2_DFR <- load_EEG_data("DFR", "ERSPS_FC2")
ERSPS_AFz_DFR <- load_EEG_data("DFR", "ERSPS_AFz")
ERPS_O1_DFR <- load_EEG_data("DFR", "ERPS_O1")
ERPS_O2_DFR <- load_EEG_data("DFR", "ERPS_O2")
ERPS_PO7_DFR <- load_EEG_data("DFR", "ERPS_PO7")
ERPS_PO8_DFR <- load_EEG_data("DFR", "ERPS_PO8")
ERPS_Oavg_DFR <- average_electrodes(list(ERPS_O1_DFR, ERPS_O2_DFR))
ERPS_POavg_DFR <- average_electrodes(list(ERPS_PO7_DFR, ERPS_PO8_DFR))
ERPS_Pavg_DFR <- average_electrodes(list(ERPS_Pz_DFR,ERPS_P1_DFR, ERPS_P2_DFR,ERPS_POz_DFR,ERPS_CP1_DFR, ERPS_CP2_DFR))
ERSPS_Favg_DFR <- average_electrodes_spectrum(list(ERSPS_Fz_DFR, ERSPS_F1_DFR, ERSPS_F2_DFR, ERSPS_AFz_DFR, ERSPS_FC1_DFR, ERSPS_FC2_DFR))
#save(list=c("CDA","CDA_fMRI","ERPS_times_DFR", "ERPS_times_LCD", "ERSPS_times_DFR", "ERSPS_times_LCD", "ERSPS_freqs", "ERPS_midOccip_LCD", "ERSPS_midOccip_LCD", "ERSPS_Oz_LCD","ERSPS_O2_LCD","ERSPS_O1_LCD", "ERPS_Pz_LCD","ERPS_Fz_LCD", "ERPS_midOccip_DFR", "ERSPS_midOccip_DFR", "ERSPS_Oz_DFR","ERPS_Pz_DFR"), file="data/newEEG_data.RData")
rects <- data.frame(xstart=c(0,5500),xend=c(2500,7000),col = "gray")If we remove EEG from the span calculation, 10% of the subjects change their span group classification.
check_span_groups <- constructs_fMRI
check_span_groups<- check_span_groups[order(check_span_groups$omnibus_span_no_DFR_MRI),]
check_span_groups$without_MRI <- "low"
check_span_groups$without_MRI[57] <- "not_incl"
check_span_groups$without_MRI[58:113] <- "med"
check_span_groups$without_MRI[114] <- "not_incl"
check_span_groups$without_MRI[115:170] <- "high"
check_span_groups<- check_span_groups[order(check_span_groups$omnibus_span_no_DFR),]
check_span_groups$without_EEG <- "low"
check_span_groups$without_EEG[57] <- "not_incl"
check_span_groups$without_EEG[58:113] <- "med"
check_span_groups$without_EEG[114] <- "not_incl"
check_span_groups$without_EEG[115:170] <- "high"
sum(check_span_groups$without_MRI != check_span_groups$without_EEG, na.rm=TRUE)## [1] 17check_span_groups <- merge(check_span_groups, p200_demographics)
colnames(check_span_groups)[10] <- "level"
WM_groups_no_EEG <- list(high = check_span_groups %>% filter(level == "high"),
med = check_span_groups %>% filter(level == "med"),
low = check_span_groups %>% filter(level == "low"))
WM_groups_no_EEG[["all"]] <- rbind(WM_groups_no_EEG[["low"]],WM_groups_no_EEG[["med"]],WM_groups_no_EEG[["high"]] )ERP Data
avg_ERPs_for_plot <- list(
midOccip_DFR = data.frame(high_load = apply(ERPS_midOccip_DFR[["high_load"]][,2:1922], 2, mean),
low_load = apply(ERPS_midOccip_DFR[["low_load"]][,2:1922], 2, mean),
load_effect = apply(ERPS_midOccip_DFR[["load_effect"]][,2:1922], 2, mean),
time =ERPS_times_DFR),
Pavg_DFR = data.frame(high_load = apply(ERPS_Pavg_DFR[["high_load"]][,2:1922], 2, mean),
low_load = apply(ERPS_Pavg_DFR[["low_load"]][,2:1922], 2, mean),
load_effect = apply(ERPS_Pavg_DFR[["load_effect"]][,2:1922], 2, mean),
time =ERPS_times_DFR),
Oavg_DFR = data.frame(high_load = apply(ERPS_Oavg_DFR[["high_load"]][,2:1922], 2, mean),
low_load = apply(ERPS_Oavg_DFR[["low_load"]][,2:1922], 2, mean),
load_effect = apply(ERPS_Oavg_DFR[["load_effect"]][,2:1922], 2, mean),
time =ERPS_times_DFR),
POavg_DFR = data.frame(high_load = apply(ERPS_POavg_DFR[["high_load"]][,2:1922], 2, mean),
low_load = apply(ERPS_POavg_DFR[["low_load"]][,2:1922], 2, mean),
load_effect = apply(ERPS_POavg_DFR[["load_effect"]][,2:1922], 2, mean),
time =ERPS_times_DFR)
)plot_list <- list()
for (cluster in seq.int(1,length(avg_ERPs_for_plot))){
plot_list[[names(avg_ERPs_for_plot)[cluster]]][["indiv_loads"]] <- ggplot(data = avg_ERPs_for_plot[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=high_load))+
geom_line(aes(x=time,y=low_load),linetype="dotted")+
ylab("ERP")+
ggtitle("Low Load vs High Load")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
plot_list[[names(avg_ERPs_for_plot)[cluster]]][["LE"]] <- ggplot(data = avg_ERPs_for_plot[[cluster]])+
geom_line(aes(x=time,y=load_effect))+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
ylab("ERP")+
ggtitle("Load Effect")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
}avg_P <- (avg_ERPs_for_plot[["Pavg_DFR"]]$high_load + avg_ERPs_for_plot[["Pavg_DFR"]]$low_load)/2
avg_O <- (avg_ERPs_for_plot[["Oavg_DFR"]]$high_load + avg_ERPs_for_plot[["Oavg_DFR"]]$low_load)/2
avg_PO <- (avg_ERPs_for_plot[["POavg_DFR"]]$high_load + avg_ERPs_for_plot[["POavg_DFR"]]$low_load)/2
avg_plots <- data.frame(time = avg_ERPs_for_plot[["Pavg_DFR"]]$time, avg_P, avg_O, avg_PO)
ggplot(data = avg_plots)+
geom_line(aes(x=time,y=avg_P))+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
ylab("ERP")+
ggtitle("Average P electrodes ERP")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
ggplot(data = avg_plots)+
geom_line(aes(x=time,y=avg_O))+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
ylab("ERP")+
ggtitle("Average O electrodes ERP")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
ggplot(data = avg_plots)+
geom_line(aes(x=time,y=avg_PO))+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
ylab("ERP")+
ggtitle("Average PO electrodes ERP")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
cue_P3_range <- c(which(avg_plots$avg_P == max(avg_plots$avg_P[229:290]))-6 , which(avg_plots$avg_P == max(avg_plots$avg_P[229:290]))+6)
probe_P3_range <- c(which(avg_plots$avg_P == max(avg_plots$avg_P[1664:1680]))-6 , which(avg_plots$avg_P == max(avg_plots$avg_P[1664:1680]))+6)
cue_n170_range <- c(which(avg_plots$avg_O == min(avg_plots$avg_O[129:198]))-6, which(avg_plots$avg_O == min(avg_plots$avg_O[129:198]))+6)
probe_n170_range <- c(which(avg_plots$avg_O == min(avg_plots$avg_O[1550:1677]))-6,which(avg_plots$avg_O == min(avg_plots$avg_O[1550:1677]))+6)All Subjects
During both the cue and probe, we see significant load effects in the N170 components in the average of the O1 and O2 electrodes and the PO7 and PO8 averages. We also see a load effect in the P3 in the parietal electrode average. Both of these show a larger amplitude in the low load than high load.
plot_list[["Pavg_DFR"]][["indiv_loads"]] + plot_list[["Pavg_DFR"]][["LE"]] +
plot_annotation(title="P avg - DFR")
plot_list[["Oavg_DFR"]][["indiv_loads"]] + plot_list[["Oavg_DFR"]][["LE"]] +
plot_annotation(title="O avg - DFR")
plot_list[["POavg_DFR"]][["indiv_loads"]] + plot_list[["POavg_DFR"]][["LE"]] +
plot_annotation(title="PO avg - DFR")
cue_average_midOccip_n170 <- select_period_average(ERPS_midOccip_DFR,avg_plots$time[cue_n170_range[1]],avg_plots$time[cue_n170_range[2]],ERPS_times_DFR)
cue_average_O_n170 <- select_period_average(ERPS_Oavg_DFR,avg_plots$time[cue_n170_range[1]],avg_plots$time[cue_n170_range[2]],ERPS_times_DFR)
cue_average_PO_n170 <- select_period_average(ERPS_POavg_DFR,avg_plots$time[cue_n170_range[1]],avg_plots$time[cue_n170_range[2]],ERPS_times_DFR)
cue_average_P3 <- select_period_average(ERPS_Pavg_DFR,avg_plots$time[cue_P3_range[1]],avg_plots$time[cue_P3_range[2]],ERPS_times_DFR)
probe_average_midOccip_n170 <- select_period_average(ERPS_midOccip_DFR,avg_plots$time[probe_n170_range[1]],avg_plots$time[probe_n170_range[2]],ERPS_times_DFR)
probe_average_O_n170 <- select_period_average(ERPS_Oavg_DFR,avg_plots$time[probe_n170_range[1]],avg_plots$time[probe_n170_range[2]],ERPS_times_DFR)
probe_average_PO_n170 <- select_period_average(ERPS_POavg_DFR,avg_plots$time[probe_n170_range[1]],avg_plots$time[probe_n170_range[2]],ERPS_times_DFR)
probe_average_P3 <- select_period_average(ERPS_Pavg_DFR,avg_plots$time[probe_P3_range[1]],avg_plots$time[probe_P3_range[2]],ERPS_times_DFR)t.test(cue_average_O_n170$load_effect,mu=0)##
## One Sample t-test
##
## data: cue_average_O_n170$load_effect
## t = -5.2605, df = 189, p-value = 3.871e-07
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.8283886 -0.3765553
## sample estimates:
## mean of x
## -0.602472t.test(cue_average_PO_n170$load_effect,mu=0)##
## One Sample t-test
##
## data: cue_average_PO_n170$load_effect
## t = -6.9534, df = 189, p-value = 5.642e-11
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -1.0704404 -0.5973194
## sample estimates:
## mean of x
## -0.8338799t.test(cue_average_P3$load_effect,mu=0)##
## One Sample t-test
##
## data: cue_average_P3$load_effect
## t = -5.1679, df = 189, p-value = 5.988e-07
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.6709431 -0.3002420
## sample estimates:
## mean of x
## -0.4855926t.test(probe_average_O_n170$load_effect,mu=0)##
## One Sample t-test
##
## data: probe_average_O_n170$load_effect
## t = 0.24254, df = 189, p-value = 0.8086
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.3504347 0.4486914
## sample estimates:
## mean of x
## 0.04912837t.test(probe_average_PO_n170$load_effect,mu=0)##
## One Sample t-test
##
## data: probe_average_PO_n170$load_effect
## t = -0.28819, df = 189, p-value = 0.7735
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.8147653 0.6070440
## sample estimates:
## mean of x
## -0.1038607t.test(probe_average_P3$load_effect,mu=0)##
## One Sample t-test
##
## data: probe_average_P3$load_effect
## t = -4.9474, df = 189, p-value = 1.657e-06
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.7978863 -0.3430026
## sample estimates:
## mean of x
## -0.5704444split_ERPS_midOccip_DFR <- split_ERPs_into_groups(ERPS_midOccip_DFR, WM_groups_no_EEG, ERPS_times_DFR)
split_ERPS_Pavg_DFR <- split_ERPs_into_groups(ERPS_Pavg_DFR, WM_groups_no_EEG, ERPS_times_DFR)
split_ERPS_Oavg_DFR <- split_ERPs_into_groups(ERPS_Oavg_DFR, WM_groups_no_EEG, ERPS_times_DFR)
split_ERPS_POavg_DFR <- split_ERPs_into_groups(ERPS_POavg_DFR, WM_groups_no_EEG, ERPS_times_DFR)
ERPs_for_plot <- list(mid_Occip_DFR = split_ERPS_midOccip_DFR,
Pavg_DFR = split_ERPS_Pavg_DFR,
Oavg_DFR = split_ERPS_Oavg_DFR,
POavg_DFR = split_ERPS_POavg_DFR)split_ERPs_plot <- create_TC_for_plot(ERPs_for_plot)Split by WM groups
ggplot(data = split_ERPs_plot[["Pavg_DFR"]][["long"]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(data=split_ERPs_plot[["Pavg_DFR"]][["long"]] %>% filter(load=="high"),aes(x=Time,y=Mean,color=level)) +
geom_line(data=split_ERPs_plot[["Pavg_DFR"]][["long"]] %>% filter(load=="low"),aes(x=Time,y=Mean,color=level),linetype="dotted")+
scale_x_continuous(breaks = seq(-500,7000, by = 500))+
ylab("Mean ERP")+
ggtitle("P avg DFR")+
theme_classic()
ggplot(data=split_ERPs_plot[["Pavg_DFR"]][["long"]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(data=split_ERPs_plot[["Pavg_DFR"]][["long"]] %>% filter(load=="load_effect"),aes(x=Time,y=Mean,color=level)) +
ylab("Mean Activity") +
xlab("Time (ms)")+
geom_ribbon(data=split_ERPs_plot[["Pavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="high"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="red")+
geom_ribbon(data=split_ERPs_plot[["Pavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="med"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="green")+
geom_ribbon(data=split_ERPs_plot[["Pavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="low"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="blue")+
scale_x_continuous(breaks = seq(-500,7000, by = 500))+
ylab("Mean ERP Load Effect")+
ggtitle("P avg DFR")+
theme_classic()
ggplot(data = split_ERPs_plot[["Oavg_DFR"]][["long"]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(data=split_ERPs_plot[["Oavg_DFR"]][["long"]] %>% filter(load=="high"),aes(x=Time,y=Mean,color=level)) +
geom_line(data=split_ERPs_plot[["Oavg_DFR"]][["long"]] %>% filter(load=="low"),aes(x=Time,y=Mean,color=level),linetype="dotted")+
scale_x_continuous(breaks = seq(-500,7000, by = 500))+
ylab("Mean ERP")+
ggtitle("O avg DFR")+
theme_classic()
ggplot(data=split_ERPs_plot[["Oavg_DFR"]][["long"]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(data=split_ERPs_plot[["Oavg_DFR"]][["long"]] %>% filter(load=="load_effect"),aes(x=Time,y=Mean,color=level)) +
ylab("Mean Activity") +
xlab("Time (ms)")+
geom_ribbon(data=split_ERPs_plot[["Oavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="high"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="red")+
geom_ribbon(data=split_ERPs_plot[["Oavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="med"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="green")+
geom_ribbon(data=split_ERPs_plot[["Oavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="low"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="blue")+
scale_x_continuous(breaks = seq(-500,7000, by = 500))+
ylab("Mean ERP Load Effect")+
ggtitle("O avg DFR")+
theme_classic()
ggplot(data = split_ERPs_plot[["POavg_DFR"]][["long"]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(data=split_ERPs_plot[["POavg_DFR"]][["long"]] %>% filter(load=="high"),aes(x=Time,y=Mean,color=level)) +
geom_line(data=split_ERPs_plot[["POavg_DFR"]][["long"]] %>% filter(load=="low"),aes(x=Time,y=Mean,color=level),linetype="dotted")+
scale_x_continuous(breaks = seq(-500,7000, by = 500))+
ylab("Mean ERP")+
ggtitle("PO avg DFR")+
theme_classic()
ggplot(data=split_ERPs_plot[["POavg_DFR"]][["long"]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(data=split_ERPs_plot[["POavg_DFR"]][["long"]] %>% filter(load=="load_effect"),aes(x=Time,y=Mean,color=level)) +
ylab("Mean Activity") +
xlab("Time (ms)")+
geom_ribbon(data=split_ERPs_plot[["POavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="high"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="red")+
geom_ribbon(data=split_ERPs_plot[["POavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="med"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="green")+
geom_ribbon(data=split_ERPs_plot[["POavg_DFR"]][["long"]] %>% filter(load == "load_effect") %>% filter(level=="low"),aes(x=Time,ymin=SE_min, ymax=SE_max),alpha=.2,linetype=2,fill="blue")+
scale_x_continuous(breaks = seq(-500,7000, by = 500))+
ylab("Mean ERP Load Effect")+
ggtitle("PO avg DFR")+
theme_classic()
cue_P3_DFR_split <- split_into_groups(cue_average_P3, WM_groups_no_EEG)
cue_midOccip_n170_DFR_split <- split_into_groups(cue_average_midOccip_n170, WM_groups_no_EEG)
cue_Oavg_n170_DFR_split <- split_into_groups(cue_average_O_n170, WM_groups_no_EEG)
cue_POavg_n170_DFR_split <- split_into_groups(cue_average_PO_n170, WM_groups_no_EEG)
probe_P3_DFR_split <- split_into_groups(probe_average_P3, WM_groups_no_EEG)
probe_midOccip_n170_DFR_split <- split_into_groups(probe_average_midOccip_n170, WM_groups_no_EEG)
probe_Oavg_n170_DFR_split <- split_into_groups(probe_average_O_n170, WM_groups_no_EEG)
probe_POavg_n170_DFR_split <- split_into_groups(probe_average_PO_n170, WM_groups_no_EEG)
cue_P3_DFR_split_prepped <- prep_split_for_bar_plots(cue_P3_DFR_split)
cue_midOccip_n170_DFR_split_prepped <- prep_split_for_bar_plots(cue_midOccip_n170_DFR_split)
cue_Oavg_n170_DFR_split_prepped <- prep_split_for_bar_plots(cue_Oavg_n170_DFR_split)
cue_POavg_n170_DFR_split_prepped <- prep_split_for_bar_plots(cue_POavg_n170_DFR_split)
probe_P3_DFR_split_prepped <- prep_split_for_bar_plots(probe_P3_DFR_split)
probe_midOccip_n170_DFR_split_prepped <- prep_split_for_bar_plots(probe_midOccip_n170_DFR_split)
probe_Oavg_n170_DFR_split_prepped <- prep_split_for_bar_plots(probe_Oavg_n170_DFR_split)
probe_POavg_n170_DFR_split_prepped <- prep_split_for_bar_plots(probe_POavg_n170_DFR_split)WM_to_merge <- WM_groups_no_EEG[["all"]][,c(1,10)]
cue_midOccip_n170_DFR_anova <- merge(cue_average_midOccip_n170,WM_to_merge, by="PTID")
probe_midOccip_n170_DFR_anova <- merge(probe_average_midOccip_n170,WM_to_merge, by="PTID")
cue_Oavg_n170_DFR_anova <- merge(cue_average_O_n170,WM_to_merge, by="PTID")
probe_Oavg_n170_DFR_anova <- merge(probe_average_O_n170,WM_to_merge, by="PTID")
cue_POavg_n170_DFR_anova <- merge(cue_average_PO_n170,WM_to_merge, by="PTID")
probe_POavg_n170_DFR_anova <- merge(probe_average_PO_n170,WM_to_merge, by="PTID")
cue_P3_DFR_anova <- merge(cue_average_P3,WM_to_merge, by="PTID")
probe_P3_DFR_anova <- merge(probe_average_P3,WM_to_merge, by="PTID")Load Effect
Scatter plots
Here, we want to look to see uf there are any obvious relationshihps between span and ERP measures (removing any outliers). We don’t see anything really strong.
merge(cue_midOccip_n170_DFR_anova, constructs_fMRI) %>%
filter(load_effect < mean(load_effect)+3*(sd(load_effect))) %>%
filter(load_effect > mean(load_effect)-3*(sd(load_effect))) %>%
ggplot(aes(x=omnibus_span_no_DFR, y = load_effect)) +
geom_point()+
stat_smooth(method="lm")+
ylab("Load effect - cue n170 in midOccip")+
theme_classic()## `geom_smooth()` using formula 'y ~ x'
merge(probe_midOccip_n170_DFR_anova, constructs_fMRI) %>%
filter(load_effect < mean(load_effect)+3*(sd(load_effect))) %>%
filter(load_effect > mean(load_effect)-3*(sd(load_effect))) %>%
ggplot(aes(x=omnibus_span_no_DFR, y = load_effect)) +
geom_point()+
stat_smooth(method="lm")+
ylab("Load effect - probe n170 in midOccip")+
theme_classic()## `geom_smooth()` using formula 'y ~ x'
merge(cue_Oavg_n170_DFR_anova, constructs_fMRI) %>%
filter(load_effect < mean(load_effect)+3*(sd(load_effect))) %>%
filter(load_effect > mean(load_effect)-3*(sd(load_effect))) %>%
ggplot(aes(x=omnibus_span_no_DFR, y = load_effect)) +
geom_point()+
stat_smooth(method="lm")+
ylab("Load effect - cue Oavg in midOccip")+
theme_classic()## `geom_smooth()` using formula 'y ~ x'
merge(probe_Oavg_n170_DFR_anova, constructs_fMRI) %>%
filter(load_effect < mean(load_effect)+3*(sd(load_effect))) %>%
filter(load_effect > mean(load_effect)-3*(sd(load_effect))) %>%
ggplot(aes(x=omnibus_span_no_DFR, y = load_effect)) +
geom_point()+
stat_smooth(method="lm")+
ylab("Load effect - probe Oavg in midOccip")+
theme_classic()## `geom_smooth()` using formula 'y ~ x'
merge(cue_P3_DFR_anova, constructs_fMRI) %>%
filter(load_effect < mean(load_effect)+3*(sd(load_effect))) %>%
filter(load_effect > mean(load_effect)-3*(sd(load_effect))) %>%
ggplot(aes(x=omnibus_span_no_DFR, y = load_effect)) +
geom_point()+
stat_smooth(method="lm")+
ylab("Load effect - cue P3")+
theme_classic()## `geom_smooth()` using formula 'y ~ x'
merge(probe_P3_DFR_anova, constructs_fMRI) %>%
filter(load_effect < mean(load_effect)+3*(sd(load_effect))) %>%
filter(load_effect > mean(load_effect)-3*(sd(load_effect))) %>%
ggplot(aes(x=omnibus_span_no_DFR, y = load_effect)) +
geom_point()+
stat_smooth(method="lm")+
ylab("Load effect - probe P3")+
theme_classic()## `geom_smooth()` using formula 'y ~ x'
Bar Graphs
When we plot bar graphs, we don’t see any difference between load effects
cue_P3_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> cue_P3_LE
cue_midOccip_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> cue_midOccip_n170_LE
cue_Oavg_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> cue_Oavg_n170_LE
cue_POavg_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> cue_POavg_n170_LE
probe_P3_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> probe_P3_LE
probe_midOccip_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> probe_midOccip_n170_LE
probe_Oavg_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> probe_Oavg_n170_LE
probe_POavg_n170_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude")+
theme_classic()+
theme(aspect.ratio = 1) -> probe_POavg_n170_LE
cue_P3_LE + probe_P3_LE+
plot_annotation(title="P3 Load Effect during DFR")
cue_Oavg_n170_LE + probe_Oavg_n170_LE+
plot_annotation(title="Oavg n170 Load Effect during DFR")
cue_POavg_n170_LE + probe_POavg_n170_LE+
plot_annotation(title="POavg n170 Load Effect during DFR")
For the n170 in the cue period, low and high capacity groups are significantly different from 0, but medium capacity are not. In the probe period, no groups are significantly different from 0.
For the P3 in the cue period, all groups are significantly different from 0. In the probe, medium WMC subjects are significantly different.
t_test_cue_O_n170 <- cue_Oavg_n170_DFR_anova %>%
data.frame() %>%
group_by(level) %>%
summarise(name = unique(level), res = list(t.test(load_effect,mu=0)))## `summarise()` ungrouping output (override with `.groups` argument)names(t_test_cue_O_n170$res) <- unique(t_test_cue_O_n170$name)
t_test_cue_O_n170$res## $high
##
## One Sample t-test
##
## data: load_effect
## t = -2.19, df = 54, p-value = 0.03286
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.92463390 -0.04081225
## sample estimates:
## mean of x
## -0.4827231
##
##
## $low
##
## One Sample t-test
##
## data: load_effect
## t = -3.8483, df = 51, p-value = 0.0003325
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -1.1191636 -0.3517934
## sample estimates:
## mean of x
## -0.7354785
##
##
## $med
##
## One Sample t-test
##
## data: load_effect
## t = -1.8146, df = 53, p-value = 0.07525
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.86059437 0.04306837
## sample estimates:
## mean of x
## -0.408763t_test_probe_O_n170 <- probe_Oavg_n170_DFR_anova %>%
data.frame() %>%
group_by(level) %>%
summarise(name = unique(level), res = list(t.test(load_effect,mu=0)))## `summarise()` ungrouping output (override with `.groups` argument)names(t_test_probe_O_n170$res) <- unique(t_test_probe_O_n170$name)
t_test_probe_O_n170$res## $high
##
## One Sample t-test
##
## data: load_effect
## t = 0.41506, df = 54, p-value = 0.6797
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.9557956 1.4548566
## sample estimates:
## mean of x
## 0.2495305
##
##
## $low
##
## One Sample t-test
##
## data: load_effect
## t = 0.049195, df = 51, p-value = 0.961
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.5433795 0.5706788
## sample estimates:
## mean of x
## 0.01364963
##
##
## $med
##
## One Sample t-test
##
## data: load_effect
## t = -0.066358, df = 53, p-value = 0.9473
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.4571914 0.4279088
## sample estimates:
## mean of x
## -0.01464127t_test_cue_P3 <- cue_P3_DFR_anova %>%
data.frame() %>%
group_by(level) %>%
summarise(name = unique(level), res = list(t.test(load_effect,mu=0)))## `summarise()` ungrouping output (override with `.groups` argument)names(t_test_cue_P3$res) <- unique(t_test_cue_P3$name)
t_test_cue_P3$res## $high
##
## One Sample t-test
##
## data: load_effect
## t = -2.7646, df = 54, p-value = 0.007782
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.9148368 -0.1457156
## sample estimates:
## mean of x
## -0.5302762
##
##
## $low
##
## One Sample t-test
##
## data: load_effect
## t = -2.799, df = 51, p-value = 0.007218
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.6971910 -0.1147995
## sample estimates:
## mean of x
## -0.4059953
##
##
## $med
##
## One Sample t-test
##
## data: load_effect
## t = -2.5241, df = 53, p-value = 0.01463
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.8898522 -0.1018298
## sample estimates:
## mean of x
## -0.495841t_test_probe_P3 <- probe_P3_DFR_anova %>%
data.frame() %>%
group_by(level) %>%
summarise(name = unique(level), res = list(t.test(load_effect,mu=0)))## `summarise()` ungrouping output (override with `.groups` argument)names(t_test_probe_P3$res) <- unique(t_test_probe_P3$name)
t_test_probe_P3$res## $high
##
## One Sample t-test
##
## data: load_effect
## t = -1.8505, df = 54, p-value = 0.06971
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -1.097236 0.043928
## sample estimates:
## mean of x
## -0.5266543
##
##
## $low
##
## One Sample t-test
##
## data: load_effect
## t = -1.9751, df = 51, p-value = 0.05368
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.841447824 0.006857664
## sample estimates:
## mean of x
## -0.4172951
##
##
## $med
##
## One Sample t-test
##
## data: load_effect
## t = -3.5704, df = 53, p-value = 0.0007675
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.8822698 -0.2475663
## sample estimates:
## mean of x
## -0.564918cue_Oavg_n170_LE.aov <- aov(load_effect ~ level, data = cue_Oavg_n170_DFR_anova)
print(summary(cue_Oavg_n170_LE.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.1 1.549 0.633 0.532
## Residuals 158 386.4 2.446probe_Oavg_n170_LE.aov <- aov(load_effect ~ level, data = probe_Oavg_n170_DFR_anova)
print(summary(probe_Oavg_n170_LE.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.3 1.145 0.128 0.88
## Residuals 158 1417.0 8.968cue_POavg_n170_LE.aov <- aov(load_effect ~ level, data = cue_POavg_n170_DFR_anova)
print(summary(cue_POavg_n170_LE.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.8 1.411 0.518 0.597
## Residuals 158 430.3 2.723probe_POavg_n170_LE.aov <- aov(load_effect ~ level, data = probe_POavg_n170_DFR_anova)
print(summary(probe_POavg_n170_LE.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 40 19.95 0.69 0.503
## Residuals 158 4573 28.94cue_P3_LE.aov <- aov(load_effect ~ level, data = cue_P3_DFR_anova)
print(summary(cue_P3_LE.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.44 0.2185 0.125 0.882
## Residuals 158 275.51 1.7437probe_P3_LE.aov <- aov(load_effect ~ level, data = probe_P3_DFR_anova)
print(summary(probe_P3_LE.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.6 0.3098 0.114 0.893
## Residuals 158 430.6 2.7252ERSPs
temp_diff <- constructs_fMRI[,c(1,8)]
omnibus_span_corr <- list(
ERSPS_midOccip_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_midOccip_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_midOccip_DFR[["PTID"]], times=ERSPS_times_DFR),
ERSPS_Oz_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_Oz_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_Oz_DFR[["PTID"]], times=ERSPS_times_DFR),
ERSPS_Favg_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_Favg_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_Favg_DFR[["PTID"]], times=ERSPS_times_DFR)
)## Warning: The `x` argument of `as_tibble.matrix()` must have unique column names if `.name_repair` is omitted as of tibble 2.0.0.
## Using compatibility `.name_repair`.
## This warning is displayed once every 8 hours.
## Call `lifecycle::last_warnings()` to see where this warning was generated.temp_diff <- constructs_fMRI[,c(1,8)]
temp_diff[,2] <- temp_diff[,2]^2
colnames(temp_diff)[2] <- "span_squared"
omnibus_span_squared_corr <- list(
ERSPS_midOccip_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_midOccip_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_midOccip_DFR[["PTID"]], times=ERSPS_times_DFR),
ERSPS_Oz_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_Oz_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_Oz_DFR[["PTID"]], times=ERSPS_times_DFR),
ERSPS_Favg_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_Favg_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_Favg_DFR[["PTID"]], times=ERSPS_times_DFR)
)temp_diff <- data.frame(p200_data[p200_data$PTID %in% constructs_fMRI$PTID, c(1,7)])
DFR_L3_acc_corr <- list(
ERSPS_midOccip_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_midOccip_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_midOccip_DFR[["PTID"]], times=ERSPS_times_DFR),
ERSPS_Oz_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_Oz_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_Oz_DFR[["PTID"]], times=ERSPS_times_DFR),
ERSPS_Favg_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_Favg_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_Favg_DFR[["PTID"]], times=ERSPS_times_DFR)
)temp_diff <- data.frame(PTID=constructs_fMRI$PTID, LE = p200_delay_DFR$DFR_Load3_Load1)
DFR_fMRI_LE_corr <- list(
ERSPS_midOccip_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_midOccip_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_midOccip_DFR[["PTID"]], times=ERSPS_times_DFR),
ERSPS_Oz_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_Oz_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_Oz_DFR[["PTID"]], times=ERSPS_times_DFR),
ERSPS_Favg_DFR = corr_spectrum_to_indiv_diff(indiv_diff = temp_diff, spectrum = ERSPS_Favg_DFR[["data"]][["load_effect"]], spec_PTID = ERSPS_Favg_DFR[["PTID"]], times=ERSPS_times_DFR)
)Average over all subs
avg_ERSPS_midOccip_DFR <- mutate_for_heatmap(apply(ERSPS_midOccip_DFR[["data"]][["load_effect"]],c(1,2),mean))
avg_ERSPS_Oz_DFR <- mutate_for_heatmap(apply(ERSPS_Oz_DFR[["data"]][["load_effect"]],c(1,2),mean))
avg_ERSPS_Favg_DFR <- mutate_for_heatmap(apply(ERSPS_Favg_DFR[["data"]][["load_effect"]],c(1,2),mean))
ggplot(data=avg_ERSPS_midOccip_DFR, aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("midOccip cluster during DFR")+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(limits=c(-1.8, 1.25),breaks=seq.int(-1.5,1,0.5),low="blue",mid="white",high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Power Load Effect")+
theme_classic()
ggplot(data=avg_ERSPS_Oz_DFR, aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("Oz electrode during DFR")+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(limits=c(-1.8, 1.25),breaks=seq.int(-1.5,1,0.5),low="blue",mid="white",high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Power Load Effect")+
theme_classic()
Split by WMC
These plots give a good overview, but we will inspect each of the frequency bands more in depth later.
split_ERSPS_midOccip_DFR <- split_spectrum(ERSPS_midOccip_DFR, WM_groups_no_EEG)
split_ERSPS_Oz_DFR <- split_spectrum(ERSPS_Oz_DFR, WM_groups_no_EEG)
split_ERSPS_Favg_DFR <- split_spectrum(ERSPS_Favg_DFR, WM_groups_no_EEG)split_ERSPS_midOccip_DFR_for_plot <- list()
split_ERSPS_Oz_DFR_for_plot <- list()
split_ERSPS_Favg_DFR_for_plot <- list()
split_ERSPS_midOccip_DFR_plots <- list()
split_ERSPS_Oz_DFR_plots <- list()
split_ERSPS_Favg_DFR_plots <- list()
for (group in c("high","med","low")){
split_ERSPS_midOccip_DFR_for_plot[[group]] <- mutate_for_heatmap(split_ERSPS_midOccip_DFR[[group]][["load_effect"]])
split_ERSPS_Oz_DFR_for_plot[[group]] <- mutate_for_heatmap(split_ERSPS_Oz_DFR[[group]][["load_effect"]])
split_ERSPS_Favg_DFR_for_plot[[group]] <- mutate_for_heatmap(split_ERSPS_Favg_DFR[[group]][["load_effect"]])
split_ERSPS_midOccip_DFR_plots[[group]] <- ggplot(data=split_ERSPS_midOccip_DFR_for_plot[[group]], aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle(paste0(group, " WMC in midOccip during DFR"))+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(limits=c(-2.5, 1.75),breaks=seq.int(-2,1.5,1),low="blue",mid="white",high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Power Load Effect")+
theme_classic()
split_ERSPS_Oz_DFR_plots[[group]] <- ggplot(data=split_ERSPS_Oz_DFR_for_plot[[group]], aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle(paste0(group, " WMC in Oz during DFR"))+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(limits=c(-2.5, 1.75),breaks=seq.int(-2,1.5,1),low="blue",mid="white",high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Power Load Effect")+
theme_classic()
split_ERSPS_Favg_DFR_plots[[group]] <- ggplot(data=split_ERSPS_Favg_DFR_for_plot[[group]], aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle(paste0(group, " WMC in Favg during DFR"))+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(limits=c(-2.5, 1.75),breaks=seq.int(-2,1.5,1),low="blue",mid="white",high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Power Load Effect")+
theme_classic()
}split_ERSPS_midOccip_DFR_plots[["high"]] 
split_ERSPS_midOccip_DFR_plots[["med"]] 
split_ERSPS_midOccip_DFR_plots[["low"]]
split_ERSPS_Oz_DFR_plots[["high"]]
split_ERSPS_Oz_DFR_plots[["med"]]
split_ERSPS_Oz_DFR_plots[["low"]]
Inverted U shape relationships
Taking the linear correlation would obscure any inverted U shaped relationships - so let’s directly look for them by taking the values where abs(med) > abs(low) and abs(med) > abs(high) [taking absolute values to account for when the meaures are negative to baseline, which we see in EEG but don’t necessarily see in fMRI]
inverted_U_midOccip_ERSPS <- find_inverted_U_spectra(split_ERSPS_midOccip_DFR, apply(ERSPS_midOccip_DFR[["data"]][["load_effect"]],c(1,2),mean))
inverted_U_Oz_ERSPS <- find_inverted_U_spectra(split_ERSPS_Oz_DFR, apply(ERSPS_Oz_DFR[["data"]][["load_effect"]],c(1,2),mean))
inverted_U_Favg_ERSPS <- find_inverted_U_spectra(split_ERSPS_Favg_DFR, apply(ERSPS_Favg_DFR[["data"]][["load_effect"]],c(1,2),mean))Here, we plot the spectra averaged over all subjects, but masked for where there is an inverted U shape relationship.
ggplot(data=inverted_U_midOccip_ERSPS, aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("Inverted U relationship with WMC in midOccip cluster during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(limits=c(-2.5, 1.75),breaks=seq.int(-2,1.5,1),low="blue",mid="white",high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Power Load Effect")+
theme_classic() 
ggplot(data=inverted_U_Oz_ERSPS, aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("Inverted U relationship with WMC in Oz during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(limits=c(-2.5, 1.75),breaks=seq.int(-2,1.5,1),low="blue",mid="white",high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Power Load Effect")+
theme_classic() 
Correlation to Omnibus Span
All correlations below are calculated robustly, such that outliers in EEG data > 3SD above or below the mean are removed.
Mid occipital cluster shows a positive correlation with span during the encoding and delay period in the beta and low gamma bands, and a negative correlation in the theta through alpha bands during the entire task, but most prominently right at the beginning of the probe period.
Oz shows a strong positive correlation with alpha power during encoding and what looks to be low gamma across the entire task. There also seems to be a negative correlation with beta across the entire task, with particularly strong negative correlations right at the end of the delay period/beginning of probe period.
ggplot(data=omnibus_span_corr[["ERSPS_midOccip_DFR"]][["plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("midOccip cluster during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=omnibus_span_corr[["ERSPS_Oz_DFR"]][["plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("Oz electrode during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=omnibus_span_corr[["ERSPS_midOccip_DFR"]][["thresholded_plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("midOccip cluster during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=omnibus_span_corr[["ERSPS_Oz_DFR"]][["thresholded_plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("Oz electrode during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
Correlation to span squared
ggplot(data=omnibus_span_squared_corr[["ERSPS_midOccip_DFR"]][["plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("midOccip cluster during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=omnibus_span_squared_corr[["ERSPS_Oz_DFR"]][["plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("Oz electrode during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
With span squared (a relative proxy for an inverted U shaped relationship), we do see a positive correlation of around 0.18 in the alpha band around the end of encoding survive FDR correction.
ggplot(data=omnibus_span_squared_corr[["ERSPS_midOccip_DFR"]][["thresholded_plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("midOccip cluster during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=omnibus_span_squared_corr[["ERSPS_Oz_DFR"]][["thresholded_plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("Oz electrode during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
Correlation to DFR high load accuracy
ggplot(data=DFR_L3_acc_corr[["ERSPS_midOccip_DFR"]][["plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("midOccip cluster during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=DFR_L3_acc_corr[["ERSPS_Oz_DFR"]][["plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("Oz electrode during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=DFR_L3_acc_corr[["ERSPS_midOccip_DFR"]][["thresholded_plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("midOccip cluster during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=DFR_L3_acc_corr[["ERSPS_Oz_DFR"]][["thresholded_plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("Oz electrode during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
Correlation to DFR fMRI load effect
There seems to be a strong correlation between fMRI load effect and power in the alpha and beta band across electrodes, particularly during encoding, though it doesn’t survive FDR correction.
ggplot(data=DFR_fMRI_LE_corr[["ERSPS_midOccip_DFR"]][["plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("midOccip cluster during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=DFR_fMRI_LE_corr[["ERSPS_Oz_DFR"]][["plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("Oz electrode during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=DFR_fMRI_LE_corr[["ERSPS_midOccip_DFR"]][["thresholded_plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("midOccip cluster during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
ggplot(data=DFR_fMRI_LE_corr[["ERSPS_Oz_DFR"]][["thresholded_plot"]],aes(x=Y,y=X,fill=Z))+
geom_tile()+
ggtitle("Oz electrode during DFR")+
geom_vline(linetype = "dotted",xintercept = 11)+
geom_vline(linetype = "dotted", xintercept = 61)+
geom_vline(linetype= "dotted", xintercept = 122)+
scale_y_continuous(labels = function(x){return(round(ERSPS_freqs[x+1], digits=2))},breaks = seq(0, 76, by = 5))+
scale_x_continuous(labels = function(x){return(ERSPS_times_DFR[x+1])}, breaks = seq(0,150, by = 25))+
scale_fill_gradient2(low="blue",mid="white",
high="red") +
xlab("Time (ms)")+
ylab("Frequency")+
labs(fill="Correlation")+
theme_classic()
alpha <- list(
ERSPS_midOccip_DFR = list(
high_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["high_load"]][26:38,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["low_load"]][26:38,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["load_effect"]][26:38,,], c(2,3), mean)))),
ERSPS_Favg_DFR = list(
high_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["high_load"]][26:38,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["low_load"]][26:38,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["load_effect"]][26:38,,], c(2,3), mean)))),
ERSPS_Oz_DFR = list(
high_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["high_load"]][26:38,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["low_load"]][26:38,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["load_effect"]][26:38,,], c(2,3), mean))))
)
low_alpha <- list(
ERSPS_midOccip_DFR = list(
high_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["high_load"]][26:32,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["low_load"]][26:32,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["load_effect"]][26:32,,], c(2,3), mean)))),
ERSPS_Favg_DFR = list(
high_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["high_load"]][26:32,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["low_load"]][26:32,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["load_effect"]][26:32,,], c(2,3), mean)))),
ERSPS_Oz_DFR = list(
high_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["high_load"]][26:32,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["low_load"]][26:32,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["load_effect"]][26:32,,], c(2,3), mean))))
)
high_alpha <- list(
ERSPS_midOccip_DFR = list(
high_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["high_load"]][32:38,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["low_load"]][32:38,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["load_effect"]][32:38,,], c(2,3), mean)))),
ERSPS_Favg_DFR = list(
high_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["high_load"]][32:38,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["low_load"]][32:38,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["load_effect"]][32:38,,], c(2,3), mean)))),
ERSPS_Oz_DFR = list(
high_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["high_load"]][32:38,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["low_load"]][32:38,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["load_effect"]][32:38,,], c(2,3), mean))))
)
beta <- list(
ERSPS_midOccip_DFR = list(
high_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["high_load"]][37:45,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["low_load"]][37:45,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["load_effect"]][37:45,,], c(2,3), mean)))),
ERSPS_Favg_DFR = list(
high_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["high_load"]][37:45,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["low_load"]][37:45,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["load_effect"]][37:45,,], c(2,3), mean)))),
ERSPS_Oz_DFR = list(
high_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["high_load"]][37:45,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["low_load"]][37:45,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["load_effect"]][37:45,,], c(2,3), mean))))
)
low_gamma <- list(
ERSPS_midOccip_DFR = list(
high_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["high_load"]][61:75,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["low_load"]][61:75,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_midOccip_DFR$PTID,t(apply(ERSPS_midOccip_DFR[["data"]][["load_effect"]][61:75,,], c(2,3), mean)))),
ERSPS_Favg_DFR = list(
high_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["high_load"]][61:75,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["low_load"]][61:75,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Favg_DFR$PTID,t(apply(ERSPS_Favg_DFR[["data"]][["load_effect"]][61:75,,], c(2,3), mean)))),
ERSPS_Oz_DFR = list(
high_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["high_load"]][61:75,,], c(2,3), mean))),
low_load = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["low_load"]][61:75,,], c(2,3), mean))),
load_effect = data.frame(PTID = ERSPS_Oz_DFR$PTID,t(apply(ERSPS_Oz_DFR[["data"]][["load_effect"]][61:75,,], c(2,3), mean))))
)alpha_list <- list(
ERSPS_midOccip_DFR = data.frame(
high_load = apply(alpha[["ERSPS_midOccip_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(alpha[["ERSPS_midOccip_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(alpha[["ERSPS_midOccip_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Oz_DFR = data.frame(
high_load = apply(alpha[["ERSPS_Oz_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(alpha[["ERSPS_Oz_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(alpha[["ERSPS_Oz_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Favg_DFR = data.frame(
high_load = apply(alpha[["ERSPS_Favg_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(alpha[["ERSPS_Favg_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(alpha[["ERSPS_Favg_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR)
)
low_alpha_list <- list(
ERSPS_midOccip_DFR = data.frame(
high_load = apply(low_alpha[["ERSPS_midOccip_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(low_alpha[["ERSPS_midOccip_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(low_alpha[["ERSPS_midOccip_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Oz_DFR = data.frame(
high_load = apply(low_alpha[["ERSPS_Oz_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(low_alpha[["ERSPS_Oz_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(low_alpha[["ERSPS_Oz_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Favg_DFR = data.frame(
high_load = apply(low_alpha[["ERSPS_Favg_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(low_alpha[["ERSPS_Favg_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(low_alpha[["ERSPS_Favg_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR)
)
high_alpha_list <- list(
ERSPS_midOccip_DFR = data.frame(
high_load = apply(high_alpha[["ERSPS_midOccip_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(high_alpha[["ERSPS_midOccip_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(high_alpha[["ERSPS_midOccip_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Oz_DFR = data.frame(
high_load = apply(high_alpha[["ERSPS_Oz_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(high_alpha[["ERSPS_Oz_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(high_alpha[["ERSPS_Oz_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Favg_DFR = data.frame(
high_load = apply(high_alpha[["ERSPS_Favg_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(high_alpha[["ERSPS_Favg_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(high_alpha[["ERSPS_Favg_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR)
)
beta_list <- list(
ERSPS_midOccip_DFR = data.frame(
high_load = apply(beta[["ERSPS_midOccip_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(beta[["ERSPS_midOccip_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(beta[["ERSPS_midOccip_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Oz_DFR = data.frame(
high_load = apply(beta[["ERSPS_Oz_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(beta[["ERSPS_Oz_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(beta[["ERSPS_Oz_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Favg_DFR = data.frame(
high_load = apply(beta[["ERSPS_Favg_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(beta[["ERSPS_Favg_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(beta[["ERSPS_Favg_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR)
)
low_gamma_list <- list(
ERSPS_midOccip_DFR = data.frame(
high_load = apply(low_gamma[["ERSPS_midOccip_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(low_gamma[["ERSPS_midOccip_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(low_gamma[["ERSPS_midOccip_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Oz_DFR = data.frame(
high_load = apply(low_gamma[["ERSPS_Oz_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(low_gamma[["ERSPS_Oz_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(low_gamma[["ERSPS_Oz_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR),
ERSPS_Favg_DFR = data.frame(
high_load = apply(low_gamma[["ERSPS_Favg_DFR"]][["high_load"]][,2:153], 2, mean),
low_load = apply(low_gamma[["ERSPS_Favg_DFR"]][["low_load"]][,2:153], 2, mean),
load_effect = apply(low_gamma[["ERSPS_Favg_DFR"]][["load_effect"]][,2:153], 2, mean),
time = ERSPS_times_DFR)
)alpha_plot_list <- list()
low_alpha_plot_list <- list()
high_alpha_plot_list <- list()
beta_plot_list <- list()
low_gamma_plot_list <- list()
for (cluster in seq.int(1,length(alpha))){
alpha_plot_list[[names(alpha_list)[cluster]]][["indiv_loads"]] <- ggplot(data = alpha_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=high_load))+
geom_line(aes(x=time,y=low_load),linetype="dotted")+
ylab("alpha power")+
ggtitle("Low Load vs High Load")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
low_alpha_plot_list[[names(low_alpha_list)[cluster]]][["indiv_loads"]] <- ggplot(data = low_alpha_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=high_load))+
geom_line(aes(x=time,y=low_load),linetype="dotted")+
ylab("Low Alpha power")+
ggtitle("Low Load vs High Load")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
high_alpha_plot_list[[names(high_alpha_list)[cluster]]][["indiv_loads"]] <- ggplot(data = high_alpha_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=high_load))+
geom_line(aes(x=time,y=high_load),linetype="dotted")+
ylab("High Alpha power")+
ggtitle("Low Load vs High Load")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
alpha_plot_list[[names(alpha_list)[cluster]]][["LE"]] <- ggplot(data = alpha_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=load_effect))+
ylab("alpha power")+
ggtitle("Load Effect")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
low_alpha_plot_list[[names(low_alpha_list)[cluster]]][["LE"]] <- ggplot(data = low_alpha_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=load_effect))+
ylab("low_alpha power")+
ggtitle("Load Effect")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
high_alpha_plot_list[[names(high_alpha_list)[cluster]]][["LE"]] <- ggplot(data = high_alpha_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=load_effect))+
ylab("high_alpha power")+
ggtitle("Load Effect")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
}
for (cluster in seq.int(1,length(beta))){
beta_plot_list[[names(beta_list)[cluster]]][["indiv_loads"]] <- ggplot(data = beta_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=high_load))+
geom_line(aes(x=time,y=low_load),linetype="dotted")+
ylab("beta power")+
ggtitle("Low Load vs High Load")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
beta_plot_list[[names(beta_list)[cluster]]][["LE"]] <- ggplot(data = beta_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=load_effect))+
ylab("beta power")+
ggtitle("Load Effect")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
}
for (cluster in seq.int(1,length(low_gamma))){
low_gamma_plot_list[[names(low_gamma_list)[cluster]]][["indiv_loads"]] <- ggplot(data = low_gamma_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=high_load))+
geom_line(aes(x=time,y=low_load),linetype="dotted")+
ylab("low_gamma power")+
ggtitle("Low Load vs High Load")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
low_gamma_plot_list[[names(low_gamma_list)[cluster]]][["LE"]] <- ggplot(data = low_gamma_list[[cluster]])+
geom_rect(data=rects,aes(xmin=xstart, xmax=xend, ymin = -Inf, ymax=Inf,alpha =0.005),fill="grey",show.legend = FALSE)+
geom_line(aes(x=time,y=load_effect))+
ylab("low_gamma power")+
ggtitle("Load Effect")+
scale_x_continuous(breaks = seq(-500,7000, by = 1000))+
theme_classic()
}Alpha
All Subjects
When we look at the load effects over all subjects, we see significant load effects in the alpha band during the cue and probe period in Oz, mid Occipital cluster and F average electrodes and during the delay period in the Oz electrode. During the cue and probe periods, we see stronger power in the low load, but we see the opposite effect in the delay period.
alpha_plot_list[["ERSPS_midOccip_DFR"]][["indiv_loads"]] + alpha_plot_list[["ERSPS_midOccip_DFR"]][["LE"]] +
plot_annotation(title="midOccip - alpha during DFR")
alpha_plot_list[["ERSPS_Oz_DFR"]][["indiv_loads"]] + alpha_plot_list[["ERSPS_Oz_DFR"]][["LE"]] +
plot_annotation(title="Oz - alpha during DFR")
We only want to average over time points with signficant load effects, so pull those out. For the delay period, we’re only looking at early delay period.
alpha_mid_occip_times <- time_sig_load_effects(alpha[["ERSPS_midOccip_DFR"]][["load_effect"]], ERSPS_times_DFR)
alpha_Oz_times <- time_sig_load_effects(alpha[["ERSPS_Oz_DFR"]][["load_effect"]], ERSPS_times_DFR)
high_alpha_mid_occip_times <- time_sig_load_effects(high_alpha[["ERSPS_midOccip_DFR"]][["load_effect"]], ERSPS_times_DFR)
high_alpha_Oz_times <- time_sig_load_effects(high_alpha[["ERSPS_Oz_DFR"]][["load_effect"]], ERSPS_times_DFR)
low_alpha_mid_occip_times <- time_sig_load_effects(low_alpha[["ERSPS_midOccip_DFR"]][["load_effect"]], ERSPS_times_DFR)
low_alpha_Oz_times <- time_sig_load_effects(low_alpha[["ERSPS_Oz_DFR"]][["load_effect"]], ERSPS_times_DFR)alpha_cue_average_midOccip <- select_period_average(alpha[["ERSPS_midOccip_DFR"]],843.75000,2531.25,ERSPS_times_DFR)
alpha_cue_average_Oz <- select_period_average(alpha[["ERSPS_Oz_DFR"]],1140.62500,2531.25,ERSPS_times_DFR)
alpha_delay_average_midOccip <- select_period_average(alpha[["ERSPS_midOccip_DFR"]],2531.25,3175.78125,ERSPS_times_DFR)
alpha_delay_average_Oz <- select_period_average(alpha[["ERSPS_Oz_DFR"]],2531.25,3078.12500,ERSPS_times_DFR)
alpha_probe_average_midOccip <- select_period_average(alpha[["ERSPS_midOccip_DFR"]],6007.81250,6800.78125,ERSPS_times_DFR)
alpha_probe_average_Oz <- select_period_average(alpha[["ERSPS_Oz_DFR"]],6007.81250,6902.34375,ERSPS_times_DFR)low_alpha_cue_average_midOccip <- select_period_average(low_alpha[["ERSPS_midOccip_DFR"]],890.62500,2531.25,ERSPS_times_DFR)
low_alpha_cue_average_Oz <- select_period_average(low_alpha[["ERSPS_Oz_DFR"]],1390.62500,2531.25,ERSPS_times_DFR)
low_alpha_delay_average_midOccip <- select_period_average(low_alpha[["ERSPS_midOccip_DFR"]],2531.25,3324.21875,ERSPS_times_DFR)
low_alpha_delay_average_Oz <- select_period_average(low_alpha[["ERSPS_Oz_DFR"]],2531.25,3226.56250,ERSPS_times_DFR)
low_alpha_probe_average_midOccip <- select_period_average(low_alpha[["ERSPS_midOccip_DFR"]],6007.81250,6851.56250,ERSPS_times_DFR)
low_alpha_probe_average_Oz <- select_period_average(low_alpha[["ERSPS_Oz_DFR"]],5910.15625,7000.00000,ERSPS_times_DFR)high_alpha_cue_average_midOccip <- select_period_average(high_alpha[["ERSPS_midOccip_DFR"]],792.96875,2531.25,ERSPS_times_DFR)
high_alpha_cue_average_Oz <- select_period_average(high_alpha[["ERSPS_Oz_DFR"]],1042.96875,2531.25,ERSPS_times_DFR)
high_alpha_delay_average_midOccip <- select_period_average(high_alpha[["ERSPS_midOccip_DFR"]],2531.25,3078.12500,ERSPS_times_DFR)
high_alpha_delay_average_Oz <- select_period_average(high_alpha[["ERSPS_Oz_DFR"]],2531.25,2976.56250,ERSPS_times_DFR)
high_alpha_late_delay_average_midOccip <- select_period_average(high_alpha[["ERSPS_midOccip_DFR"]],3375.00000,5261.71875,ERSPS_times_DFR)
high_alpha_late_delay_average_Oz <- select_period_average(high_alpha[["ERSPS_Oz_DFR"]],3226.56250,
5261.71875,ERSPS_times_DFR)
high_alpha_probe_average_midOccip <- select_period_average(high_alpha[["ERSPS_midOccip_DFR"]],6007.81250,6800.78125,ERSPS_times_DFR)
high_alpha_probe_average_Oz <- select_period_average(high_alpha[["ERSPS_Oz_DFR"]],6058.59375,6851.56250,ERSPS_times_DFR)t.test(alpha_cue_average_midOccip$load_effect,mu=0)##
## One Sample t-test
##
## data: alpha_cue_average_midOccip$load_effect
## t = -10.688, df = 177, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -1.2794605 -0.8806336
## sample estimates:
## mean of x
## -1.080047t.test(alpha_cue_average_Oz$load_effect,mu=0)##
## One Sample t-test
##
## data: alpha_cue_average_Oz$load_effect
## t = -7.5737, df = 189, p-value = 1.565e-12
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.8895789 -0.5219415
## sample estimates:
## mean of x
## -0.7057602t.test(alpha_delay_average_midOccip$load_effect,mu=0)##
## One Sample t-test
##
## data: alpha_delay_average_midOccip$load_effect
## t = -8.1718, df = 177, p-value = 5.61e-14
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -1.0746931 -0.6565923
## sample estimates:
## mean of x
## -0.8656427t.test(alpha_delay_average_Oz$load_effect,mu=0)##
## One Sample t-test
##
## data: alpha_delay_average_Oz$load_effect
## t = -8.0186, df = 189, p-value = 1.089e-13
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.8903460 -0.5387768
## sample estimates:
## mean of x
## -0.7145614t.test(alpha_probe_average_midOccip$load_effect,mu=0)##
## One Sample t-test
##
## data: alpha_probe_average_midOccip$load_effect
## t = -5.8461, df = 177, p-value = 2.373e-08
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.9071712 -0.4492800
## sample estimates:
## mean of x
## -0.6782256t.test(alpha_probe_average_Oz$load_effect,mu=0)##
## One Sample t-test
##
## data: alpha_probe_average_Oz$load_effect
## t = -7.4267, df = 189, p-value = 3.714e-12
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -1.1191573 -0.6494088
## sample estimates:
## mean of x
## -0.8842831ERSP_behav_corr_data <- constructs_fMRI[,c(1,8)]
ERSP_behav_corr_data$LE <- p200_delay_DFR$DFR_Load3_Load1
ERSP_behav_corr_data[,4] <- p200_data[p200_data$PTID %in% constructs_fMRI$PTID, 7]
alpha_data <- Reduce(function(x,y) merge(x,y, by="PTID"),
list(alpha_cue_average_midOccip[,c(1,4)], alpha_cue_average_Oz[,c(1,4)],
alpha_delay_average_Oz[,c(1,4)],alpha_probe_average_midOccip[,c(1,4)],
alpha_probe_average_Oz[,c(1,4)]))## Warning in merge.data.frame(x, y, by = "PTID"): column names 'load_effect.x',
## 'load_effect.y' are duplicated in the result
## Warning in merge.data.frame(x, y, by = "PTID"): column names 'load_effect.x',
## 'load_effect.y' are duplicated in the resultcolnames(alpha_data) <- c("PTID", "cue_midOccip", "cue_Oz", "delay_Oz", "probe_midOccip", "probe_Oz")
alpha_behav <- merge(ERSP_behav_corr_data,alpha_data, by = "PTID")
for (col in seq.int(5,9)){
alpha_behav[((alpha_behav[,col] > mean(alpha_behav[,col])+3*sd(alpha_behav[,col])) |
(alpha_behav[,col] < mean(alpha_behav[,col])-3*sd(alpha_behav[,col]))), col] <- NA
}
for (col in seq.int(5,9)){
print(ggplot(data = alpha_behav, aes_string(x="omnibus_span_no_DFR", y = colnames(alpha_behav)[col]))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1))
print(colnames(alpha_behav)[col])
print(cor.test(alpha_behav$omnibus_span_no_DFR, alpha_behav[,col]))
}## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
## [1] "cue_midOccip"
##
## Pearson's product-moment correlation
##
## data: alpha_behav$omnibus_span_no_DFR and alpha_behav[, col]
## t = -1.2174, df = 150, p-value = 0.2253
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.25411529 0.06124894
## sample estimates:
## cor
## -0.09891628## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).
## Warning: Removed 1 rows containing missing values (geom_point).
## [1] "cue_Oz"
##
## Pearson's product-moment correlation
##
## data: alpha_behav$omnibus_span_no_DFR and alpha_behav[, col]
## t = 0.45722, df = 150, p-value = 0.6482
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1226234 0.1953461
## sample estimates:
## cor
## 0.03730556## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
## [1] "delay_Oz"
##
## Pearson's product-moment correlation
##
## data: alpha_behav$omnibus_span_no_DFR and alpha_behav[, col]
## t = -0.26668, df = 149, p-value = 0.7901
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1809393 0.1383691
## sample estimates:
## cor
## -0.02184212## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).
## [1] "probe_midOccip"
##
## Pearson's product-moment correlation
##
## data: alpha_behav$omnibus_span_no_DFR and alpha_behav[, col]
## t = -1.8613, df = 149, p-value = 0.06468
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.303168319 0.009210906
## sample estimates:
## cor
## -0.1507393## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).
## [1] "probe_Oz"
##
## Pearson's product-moment correlation
##
## data: alpha_behav$omnibus_span_no_DFR and alpha_behav[, col]
## t = -0.56958, df = 148, p-value = 0.5698
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2054896 0.1143503
## sample estimates:
## cor
## -0.04676825alphas_for_plot <- list()
betas_for_plot <- list()
low_alphas_for_plot <- list()
high_alphas_for_plot <- list()
low_gammas_for_plot <- list()
load_list <- c("low_load", "high_load", "load_effect")
ERSPS_list <- list(
ERSPS_midOccip_DFR = ERSPS_midOccip_DFR,
ERSPS_Oz_DFR = ERSPS_Oz_DFR)
for (cluster in seq.int(1,2)){
temp <- list()
for (load in seq.int(1,3)){
temp[[load_list[load]]] <- alpha[[cluster]][[load]]
}
if (substr(names(alpha)[cluster],nchar(names(alpha)[cluster])-4,nchar(names(alpha)[cluster])-2) == "LCD"){
alphas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups_no_EEG, ERSPS_times_LCD)
} else{
alphas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups_no_EEG, ERSPS_times_DFR)
}
}
for (cluster in seq.int(1,2)){
temp <- list()
for (load in seq.int(1,3)){
temp[[load_list[load]]] <- low_alpha[[cluster]][[load]]
}
if (substr(names(low_alpha)[cluster],nchar(names(low_alpha)[cluster])-4,nchar(names(low_alpha)[cluster])-2) == "LCD"){
low_alphas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups_no_EEG, ERSPS_times_LCD)
} else{
low_alphas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups_no_EEG, ERSPS_times_DFR)
}
}
for (cluster in seq.int(1,2)){
temp <- list()
for (load in seq.int(1,3)){
temp[[load_list[load]]] <- high_alpha[[cluster]][[load]]
}
if (substr(names(high_alpha)[cluster],nchar(names(high_alpha)[cluster])-4,nchar(names(high_alpha)[cluster])-2) == "LCD"){
high_alphas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups_no_EEG, ERSPS_times_LCD)
} else{
high_alphas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups_no_EEG, ERSPS_times_DFR)
}
}
for (cluster in seq.int(1,2)){
temp <- list()
for (load in seq.int(1,3)){
temp[[load_list[load]]] <- beta[[cluster]][[load]]
}
if (substr(names(beta)[cluster],nchar(names(beta)[cluster])-4,nchar(names(beta)[cluster])-2) == "LCD"){
betas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups_no_EEG, ERSPS_times_LCD)
} else{
betas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups_no_EEG, ERSPS_times_DFR)
}
}
for (cluster in seq.int(1,2)){
temp <- list()
for (load in seq.int(1,3)){
temp[[load_list[load]]] <- low_gamma[[cluster]][[load]]
}
if (substr(names(low_gamma)[cluster],nchar(names(low_gamma)[cluster])-4,nchar(names(low_gamma)[cluster])-2) == "LCD"){
low_gammas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups_no_EEG, ERSPS_times_LCD)
} else{
low_gammas_for_plot[[names(ERSPS_list)[cluster]]] <- split_ERPs_into_groups(temp, WM_groups_no_EEG, ERSPS_times_DFR)
}
}By WM groups
When we split into the working memory capacity groups, we see differences across capacity in the mid occipital cluster during probe period, where we see a more negative load effect in the high capacity group vs the medium capacity group. We also see a significant difference between the low and high capacity subjects in the raw high load power, where the high capacity subjects show more negative power than the low load subjects.
We also can see an inverted U shape during the delay period in the Oz electrode, though it isn’t significant.
split_alphas_plot <- create_TC_for_plot(alphas_for_plot)
split_low_alphas_plot <- create_TC_for_plot(low_alphas_for_plot)
split_high_alphas_plot <- create_TC_for_plot(high_alphas_for_plot)
split_betas_plot <- create_TC_for_plot(betas_for_plot)
split_low_gammas_plot <- create_TC_for_plot(low_gammas_for_plot)All Alpha
midOccip_DFR_alpha <- paired_freq_plot(split_alphas_plot[["ERSPS_midOccip_DFR"]][["long"]], c("high","low", "load_effect"))
Oz_DFR_alpha <- paired_freq_plot(split_alphas_plot[["ERSPS_Oz_DFR"]][["long"]], c("high","low", "load_effect"))
midOccip_DFR_alpha[[1]] + midOccip_DFR_alpha[[2]]+
plot_annotation(title="midOccip - alpha during DFR")+
plot_layout(guides="collect")
Oz_DFR_alpha[[1]] + Oz_DFR_alpha[[2]]+
plot_annotation(title="Oz - alpha during DFR")+
plot_layout(guides="collect")
alpha_cue_Oz_DFR_split <- split_into_groups(alpha_cue_average_Oz, WM_groups_no_EEG)
alpha_cue_midOccip_DFR_split <- split_into_groups(alpha_cue_average_midOccip, WM_groups_no_EEG)
alpha_delay_Oz_DFR_split <- split_into_groups(alpha_delay_average_Oz, WM_groups_no_EEG)
alpha_delay_midOccip_DFR_split <- split_into_groups(alpha_delay_average_midOccip, WM_groups_no_EEG)
alpha_probe_Oz_DFR_split <- split_into_groups(alpha_probe_average_Oz, WM_groups_no_EEG)
alpha_probe_midOccip_DFR_split <- split_into_groups(alpha_probe_average_midOccip, WM_groups_no_EEG)
alpha_cue_Oz_DFR_split_prepped <- prep_split_for_bar_plots(alpha_cue_Oz_DFR_split)
alpha_cue_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(alpha_cue_midOccip_DFR_split)
alpha_delay_Oz_DFR_split_prepped <- prep_split_for_bar_plots(alpha_delay_Oz_DFR_split)
alpha_delay_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(alpha_delay_midOccip_DFR_split)
alpha_probe_Oz_DFR_split_prepped <- prep_split_for_bar_plots(alpha_probe_Oz_DFR_split)
alpha_probe_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(alpha_probe_midOccip_DFR_split)alpha_cue_midOccip_DFR_anova <- merge(alpha_cue_average_midOccip,WM_to_merge, by="PTID")
alpha_delay_midOccip_DFR_anova <- merge(alpha_delay_average_midOccip,WM_to_merge, by="PTID")
alpha_probe_midOccip_DFR_anova <- merge(alpha_probe_average_midOccip,WM_to_merge, by="PTID")
alpha_cue_Oz_DFR_anova <- merge(alpha_cue_average_Oz,WM_to_merge, by="PTID")
alpha_delay_Oz_DFR_anova <- merge(alpha_delay_average_Oz,WM_to_merge, by="PTID")
alpha_probe_Oz_DFR_anova <- merge(alpha_probe_average_Oz,WM_to_merge, by="PTID")alpha_cue_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_cue_Oz_LE
alpha_cue_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_cue_midOccip_LE
alpha_delay_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_delay_Oz_LE
alpha_delay_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_delay_midOccip_LE
alpha_probe_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_probe_Oz_LE
alpha_probe_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> alpha_probe_midOccip_LE
alpha_cue_Oz_LE + alpha_delay_Oz_LE + alpha_probe_Oz_LE+
plot_annotation(title="Oz electrode alpha frequency Load Effect during DFR")
alpha_cue_midOccip_LE + alpha_delay_midOccip_LE + alpha_probe_midOccip_LE+
plot_annotation(title="midOccip cluster alpha frequency Load Effect during DFR")
alpha_cue_midOccip.aov <- aov(load_effect ~ level, data = alpha_cue_midOccip_DFR_anova)
print("alpha - cue midOccip")## [1] "alpha - cue midOccip"print(summary(alpha_cue_midOccip.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.34 1.169 0.626 0.536
## Residuals 148 276.20 1.866alpha_delay_midOccip.aov <- aov(load_effect ~ level, data = alpha_delay_midOccip_DFR_anova)
print("alpha - delay midOccip")## [1] "alpha - delay midOccip"print(summary(alpha_delay_midOccip.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.76 1.381 0.73 0.484
## Residuals 148 280.08 1.892alpha_probe_midOccip.aov <- aov(load_effect ~ level, data = alpha_probe_midOccip_DFR_anova)
print("alpha - probe midOccip")## [1] "alpha - probe midOccip"alpha_cue_Oz.aov <- aov(load_effect ~ level, data = alpha_cue_Oz_DFR_anova)
print("alpha - cue Oz")## [1] "alpha - cue Oz"print(summary(alpha_cue_Oz.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.19 0.0961 0.059 0.943
## Residuals 158 257.46 1.6295alpha_delay_Oz.aov <- aov(load_effect ~ level, data = alpha_delay_Oz_DFR_anova)
print("alpha - delay Oz")## [1] "alpha - delay Oz"print(summary(alpha_delay_Oz.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.26 0.6299 0.463 0.63
## Residuals 158 214.80 1.3595Low alpha
midOccip_DFR_low_alpha <- paired_freq_plot(split_low_alphas_plot[["ERSPS_midOccip_DFR"]][["long"]], c("high","low", "load_effect"))
Oz_DFR_low_alpha <- paired_freq_plot(split_low_alphas_plot[["ERSPS_Oz_DFR"]][["long"]], c("high","low", "load_effect"))
midOccip_DFR_low_alpha[[1]] + midOccip_DFR_low_alpha[[2]]+
plot_annotation(title="midOccip - low_alpha during DFR")+
plot_layout(guides="collect")
Oz_DFR_low_alpha[[1]] + Oz_DFR_low_alpha[[2]]+
plot_annotation(title="Oz - low_alpha during DFR")+
plot_layout(guides="collect")
low_alpha_cue_Oz_DFR_split <- split_into_groups(low_alpha_cue_average_Oz, WM_groups_no_EEG)
low_alpha_cue_midOccip_DFR_split <- split_into_groups(low_alpha_cue_average_midOccip, WM_groups_no_EEG)
low_alpha_delay_Oz_DFR_split <- split_into_groups(low_alpha_delay_average_Oz, WM_groups_no_EEG)
low_alpha_delay_midOccip_DFR_split <- split_into_groups(low_alpha_delay_average_midOccip, WM_groups_no_EEG)
low_alpha_probe_Oz_DFR_split <- split_into_groups(low_alpha_probe_average_Oz, WM_groups_no_EEG)
low_alpha_probe_midOccip_DFR_split <- split_into_groups(low_alpha_probe_average_midOccip, WM_groups_no_EEG)
low_alpha_cue_Oz_DFR_split_prepped <- prep_split_for_bar_plots(low_alpha_cue_Oz_DFR_split)
low_alpha_cue_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(low_alpha_cue_midOccip_DFR_split)
low_alpha_delay_Oz_DFR_split_prepped <- prep_split_for_bar_plots(low_alpha_delay_Oz_DFR_split)
low_alpha_delay_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(low_alpha_delay_midOccip_DFR_split)
low_alpha_probe_Oz_DFR_split_prepped <- prep_split_for_bar_plots(low_alpha_probe_Oz_DFR_split)
low_alpha_probe_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(low_alpha_probe_midOccip_DFR_split)low_alpha_cue_midOccip_DFR_anova <- merge(low_alpha_cue_average_midOccip,WM_to_merge, by="PTID")
low_alpha_delay_midOccip_DFR_anova <- merge(low_alpha_delay_average_midOccip,WM_to_merge, by="PTID")
low_alpha_probe_midOccip_DFR_anova <- merge(low_alpha_probe_average_midOccip,WM_to_merge, by="PTID")
low_alpha_cue_Oz_DFR_anova <- merge(low_alpha_cue_average_Oz,WM_to_merge, by="PTID")
low_alpha_delay_Oz_DFR_anova <- merge(low_alpha_delay_average_Oz,WM_to_merge, by="PTID")
low_alpha_probe_Oz_DFR_anova <- merge(low_alpha_probe_average_Oz,WM_to_merge, by="PTID")low_alpha_cue_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> low_alpha_cue_Oz_LE
low_alpha_cue_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> low_alpha_cue_midOccip_LE
low_alpha_delay_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> low_alpha_delay_Oz_LE
low_alpha_delay_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> low_alpha_delay_midOccip_LE
low_alpha_probe_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> low_alpha_probe_Oz_LE
low_alpha_probe_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> low_alpha_probe_midOccip_LE
low_alpha_cue_Oz_LE + low_alpha_delay_Oz_LE + low_alpha_probe_Oz_LE+
plot_annotation(title="Oz electrode low_alpha frequency Load Effect during DFR")
low_alpha_cue_midOccip_LE + low_alpha_delay_midOccip_LE + low_alpha_probe_midOccip_LE+
plot_annotation(title="midOccip cluster low_alpha frequency Load Effect during DFR")
low_alpha_cue_midOccip.aov <- aov(load_effect ~ level, data = low_alpha_cue_midOccip_DFR_anova)
print("low_alpha - cue midOccip")## [1] "low_alpha - cue midOccip"print(summary(low_alpha_cue_midOccip.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 4.2 2.089 0.794 0.454
## Residuals 148 389.4 2.631low_alpha_delay_midOccip.aov <- aov(load_effect ~ level, data = low_alpha_delay_midOccip_DFR_anova)
print("low_alpha - delay midOccip")## [1] "low_alpha - delay midOccip"print(summary(low_alpha_delay_midOccip.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 4.9 2.446 0.993 0.373
## Residuals 148 364.5 2.463low_alpha_probe_midOccip.aov <- aov(load_effect ~ level, data = low_alpha_probe_midOccip_DFR_anova)
print("low_alpha - probe midOccip")## [1] "low_alpha - probe midOccip"low_alpha_cue_Oz.aov <- aov(load_effect ~ level, data = low_alpha_cue_Oz_DFR_anova)
print("low_alpha - cue Oz")## [1] "low_alpha - cue Oz"print(summary(low_alpha_cue_Oz.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.2 0.081 0.038 0.963
## Residuals 158 340.9 2.158low_alpha_delay_Oz.aov <- aov(load_effect ~ level, data = low_alpha_delay_Oz_DFR_anova)
print("low_alpha - delay Oz")## [1] "low_alpha - delay Oz"print(summary(low_alpha_delay_Oz.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.42 1.212 0.731 0.483
## Residuals 158 262.00 1.658High alpha
midOccip_DFR_high_alpha <- paired_freq_plot(split_high_alphas_plot[["ERSPS_midOccip_DFR"]][["long"]], c("high","low", "load_effect"))
Oz_DFR_high_alpha <- paired_freq_plot(split_high_alphas_plot[["ERSPS_Oz_DFR"]][["long"]], c("high","low", "load_effect"))
midOccip_DFR_high_alpha[[1]] + midOccip_DFR_high_alpha[[2]]+
plot_annotation(title="midOccip - high_alpha during DFR")+
plot_layout(guides="collect")
Oz_DFR_high_alpha[[1]] + Oz_DFR_high_alpha[[2]]+
plot_annotation(title="Oz - high_alpha during DFR")+
plot_layout(guides="collect")
high_alpha_cue_Oz_DFR_split <- split_into_groups(high_alpha_cue_average_Oz, WM_groups_no_EEG)
high_alpha_cue_midOccip_DFR_split <- split_into_groups(high_alpha_cue_average_midOccip, WM_groups_no_EEG)
high_alpha_delay_Oz_DFR_split <- split_into_groups(high_alpha_delay_average_Oz, WM_groups_no_EEG)
high_alpha_delay_midOccip_DFR_split <- split_into_groups(high_alpha_delay_average_midOccip, WM_groups_no_EEG)
high_alpha_late_delay_Oz_DFR_split <- split_into_groups(high_alpha_late_delay_average_Oz, WM_groups_no_EEG)
high_alpha_late_delay_midOccip_DFR_split <- split_into_groups(high_alpha_late_delay_average_midOccip, WM_groups_no_EEG)
high_alpha_probe_Oz_DFR_split <- split_into_groups(high_alpha_probe_average_Oz, WM_groups_no_EEG)
high_alpha_probe_midOccip_DFR_split <- split_into_groups(high_alpha_probe_average_midOccip, WM_groups_no_EEG)
high_alpha_cue_Oz_DFR_split_prepped <- prep_split_for_bar_plots(high_alpha_cue_Oz_DFR_split)
high_alpha_cue_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(high_alpha_cue_midOccip_DFR_split)
high_alpha_delay_Oz_DFR_split_prepped <- prep_split_for_bar_plots(high_alpha_delay_Oz_DFR_split)
high_alpha_delay_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(high_alpha_delay_midOccip_DFR_split)
high_alpha_late_delay_Oz_DFR_split_prepped <- prep_split_for_bar_plots(high_alpha_late_delay_Oz_DFR_split)
high_alpha_late_delay_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(high_alpha_late_delay_midOccip_DFR_split)
high_alpha_probe_Oz_DFR_split_prepped <- prep_split_for_bar_plots(high_alpha_probe_Oz_DFR_split)
high_alpha_probe_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(high_alpha_probe_midOccip_DFR_split)high_alpha_cue_midOccip_DFR_anova <- merge(high_alpha_cue_average_midOccip,WM_to_merge, by="PTID")
high_alpha_delay_midOccip_DFR_anova <- merge(high_alpha_delay_average_midOccip,WM_to_merge, by="PTID")
high_alpha_late_delay_midOccip_DFR_anova <- merge(high_alpha_late_delay_average_midOccip,WM_to_merge, by="PTID")
high_alpha_probe_midOccip_DFR_anova <- merge(high_alpha_probe_average_midOccip,WM_to_merge, by="PTID")
high_alpha_cue_Oz_DFR_anova <- merge(high_alpha_cue_average_Oz,WM_to_merge, by="PTID")
high_alpha_delay_Oz_DFR_anova <- merge(high_alpha_delay_average_Oz,WM_to_merge, by="PTID")
high_alpha_late_delay_Oz_DFR_anova <- merge(high_alpha_late_delay_average_Oz,WM_to_merge, by="PTID")
high_alpha_probe_Oz_DFR_anova <- merge(high_alpha_probe_average_Oz,WM_to_merge, by="PTID")None of these are signficant - only caveat is that I haven’t removed outliers, because I wasn’t sure at what level to remove them from - averages over time? at each time point?
high_alpha_cue_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> high_alpha_cue_Oz_LE
high_alpha_cue_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> high_alpha_cue_midOccip_LE
high_alpha_delay_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> high_alpha_delay_Oz_LE
high_alpha_delay_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> high_alpha_delay_midOccip_LE
high_alpha_late_delay_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Late Delay - Oz")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> high_alpha_late_delay_Oz_LE
high_alpha_late_delay_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Late Delay - Mid Occipital")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> high_alpha_late_delay_midOccip_LE
high_alpha_probe_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> high_alpha_probe_Oz_LE
high_alpha_probe_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> high_alpha_probe_midOccip_LE
high_alpha_cue_Oz_LE + high_alpha_delay_Oz_LE + high_alpha_probe_Oz_LE+
plot_annotation(title="Oz electrode high alpha frequency Load Effect during DFR")
high_alpha_late_delay_Oz_LE
high_alpha_cue_midOccip_LE + high_alpha_delay_midOccip_LE + high_alpha_probe_midOccip_LE+
plot_annotation(title="midOccip cluster high alpha frequency Load Effect during DFR")
high_alpha_late_delay_midOccip_LE
high_alpha_cue_midOccip.aov <- aov(load_effect ~ level, data = high_alpha_cue_midOccip_DFR_anova)
print("high_alpha - cue midOccip")## [1] "high_alpha - cue midOccip"print(summary(high_alpha_cue_midOccip.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.34 0.6685 0.392 0.676
## Residuals 148 252.38 1.7052high_alpha_delay_midOccip.aov <- aov(load_effect ~ level, data = high_alpha_delay_midOccip_DFR_anova)
print("high_alpha - delay midOccip")## [1] "high_alpha - delay midOccip"print(summary(high_alpha_delay_midOccip.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.78 0.8877 0.488 0.615
## Residuals 148 269.17 1.8187high_alpha_late_delay_midOccip.aov <- aov(load_effect ~ level, data = high_alpha_late_delay_midOccip_DFR_anova)
print("high_alpha - late delay midOccip")## [1] "high_alpha - late delay midOccip"print(summary(high_alpha_late_delay_midOccip.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.89 1.443 0.879 0.417
## Residuals 148 243.06 1.642high_alpha_probe_midOccip.aov <- aov(load_effect ~ level, data = high_alpha_probe_midOccip_DFR_anova)
print("high_alpha - probe midOccip")## [1] "high_alpha - probe midOccip"print(summary(high_alpha_probe_midOccip.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 5.8 2.884 1.202 0.303
## Residuals 148 355.1 2.399high_alpha_cue_Oz.aov <- aov(load_effect ~ level, data = high_alpha_cue_Oz_DFR_anova)
print("high_alpha - cue Oz")## [1] "high_alpha - cue Oz"print(summary(high_alpha_cue_Oz.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.69 0.3433 0.192 0.826
## Residuals 158 282.80 1.7899high_alpha_delay_Oz.aov <- aov(load_effect ~ level, data = high_alpha_delay_Oz_DFR_anova)
print("high_alpha - delay Oz")## [1] "high_alpha - delay Oz"print(summary(high_alpha_delay_Oz.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.35 0.6732 0.402 0.67
## Residuals 158 264.69 1.6753high_alpha_late_delay_Oz.aov <- aov(load_effect ~ level, data = high_alpha_late_delay_Oz_DFR_anova)
print("high_alpha - late delay Oz")## [1] "high_alpha - late delay Oz"print(summary(high_alpha_late_delay_Oz.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.71 0.3552 0.243 0.784
## Residuals 158 230.52 1.4590high_alpha_late_delay_Oz.aov <- aov(load_effect ~ level, data = high_alpha_late_delay_Oz_DFR_anova)
print("high_alpha - late delay Oz")## [1] "high_alpha - late delay Oz"print(summary(high_alpha_late_delay_Oz.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.71 0.3552 0.243 0.784
## Residuals 158 230.52 1.4590Beta
All Subjects
Overall, we see differences in load effects during cue and probe in all electrodes (low > high) and in delay during mid occipital and Oz (high > low).
beta_plot_list[["ERSPS_midOccip_DFR"]][["indiv_loads"]] + beta_plot_list[["ERSPS_midOccip_DFR"]][["LE"]] +
plot_annotation(title="midOccip - beta during DFR")
beta_plot_list[["ERSPS_Oz_DFR"]][["indiv_loads"]] + beta_plot_list[["ERSPS_Oz_DFR"]][["LE"]] +
plot_annotation(title="Oz - beta during DFR")
beta_mid_occip_times <- time_sig_load_effects(beta[["ERSPS_midOccip_DFR"]][["load_effect"]], ERSPS_times_DFR)
beta_Oz_times <- time_sig_load_effects(beta[["ERSPS_Oz_DFR"]][["load_effect"]], ERSPS_times_DFR)beta_cue_average_midOccip <- select_period_average(beta[["ERSPS_midOccip_DFR"]],890.62500,2531.25,ERSPS_times_DFR)
beta_cue_average_Oz <- select_period_average(beta[["ERSPS_Oz_DFR"]],1089.84375,2531.25,ERSPS_times_DFR)
beta_delay_average_midOccip <- select_period_average(beta[["ERSPS_midOccip_DFR"]],2531.25,3128.90625,ERSPS_times_DFR)
beta_delay_average_Oz <- select_period_average(beta[["ERSPS_Oz_DFR"]],2531.25,2878.90625,ERSPS_times_DFR)
beta_probe_average_midOccip <- select_period_average(beta[["ERSPS_midOccip_DFR"]],5757.81250,6800.78125,ERSPS_times_DFR)
beta_probe_average_Oz <- select_period_average(beta[["ERSPS_Oz_DFR"]],6007.81250,6800.78125,ERSPS_times_DFR)t.test(beta_cue_average_midOccip$load_effect,mu=0)##
## One Sample t-test
##
## data: beta_cue_average_midOccip$load_effect
## t = -8.432, df = 177, p-value = 1.159e-14
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.8010191 -0.4971839
## sample estimates:
## mean of x
## -0.6491015t.test(beta_cue_average_Oz$load_effect,mu=0)##
## One Sample t-test
##
## data: beta_cue_average_Oz$load_effect
## t = -6.4893, df = 189, p-value = 7.375e-10
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.7004727 -0.3738916
## sample estimates:
## mean of x
## -0.5371821t.test(beta_delay_average_midOccip$load_effect,mu=0)##
## One Sample t-test
##
## data: beta_delay_average_midOccip$load_effect
## t = -5.4057, df = 177, p-value = 2.066e-07
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.6157004 -0.2863768
## sample estimates:
## mean of x
## -0.4510386t.test(beta_delay_average_Oz$load_effect,mu=0)##
## One Sample t-test
##
## data: beta_delay_average_Oz$load_effect
## t = -7.2266, df = 189, p-value = 1.187e-11
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.779423 -0.445158
## sample estimates:
## mean of x
## -0.6122905t.test(beta_probe_average_midOccip$load_effect,mu=0)##
## One Sample t-test
##
## data: beta_probe_average_midOccip$load_effect
## t = -7.7091, df = 177, p-value = 8.774e-13
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.9328424 -0.5525856
## sample estimates:
## mean of x
## -0.742714t.test(beta_probe_average_Oz$load_effect,mu=0)##
## One Sample t-test
##
## data: beta_probe_average_Oz$load_effect
## t = -9.446, df = 189, p-value < 2.2e-16
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -1.2988818 -0.8501101
## sample estimates:
## mean of x
## -1.074496beta_data <- Reduce(function(x,y) merge(x,y, by="PTID"),
list(beta_cue_average_midOccip[,c(1,4)], beta_cue_average_Oz[,c(1,4)],
beta_delay_average_midOccip[,c(1,4)],beta_delay_average_Oz[,c(1,4)],
beta_probe_average_midOccip[,c(1,4)],
beta_probe_average_Oz[,c(1,4)]))## Warning in merge.data.frame(x, y, by = "PTID"): column names 'load_effect.x',
## 'load_effect.y' are duplicated in the result
## Warning in merge.data.frame(x, y, by = "PTID"): column names 'load_effect.x',
## 'load_effect.y' are duplicated in the result## Warning in merge.data.frame(x, y, by = "PTID"): column names 'load_effect.x',
## 'load_effect.y', 'load_effect.x', 'load_effect.y' are duplicated in the resultcolnames(beta_data) <- c("PTID", "cue_midOccip", "cue_Oz", "delay_midOccip","delay_Oz", "probe_midOccip", "probe_Oz")
beta_behav <- merge(ERSP_behav_corr_data,beta_data, by = "PTID")
for (col in seq.int(5,9)){
beta_behav[((beta_behav[,col] > mean(beta_behav[,col])+3*sd(beta_behav[,col])) |
(beta_behav[,col] < mean(beta_behav[,col])-3*sd(beta_behav[,col]))), col] <- NA
}
for (col in seq.int(5,10)){
print(ggplot(data = beta_behav, aes_string(x="omnibus_span_no_DFR", y = colnames(beta_behav)[col]))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1))
print(colnames(beta_behav)[col])
print(cor.test(beta_behav$omnibus_span_no_DFR, beta_behav[,col]))
}## `geom_smooth()` using formula 'y ~ x'
## [1] "cue_midOccip"
##
## Pearson's product-moment correlation
##
## data: beta_behav$omnibus_span_no_DFR and beta_behav[, col]
## t = -0.25541, df = 151, p-value = 0.7988
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1788690 0.1383537
## sample estimates:
## cor
## -0.02078065## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
## [1] "cue_Oz"
##
## Pearson's product-moment correlation
##
## data: beta_behav$omnibus_span_no_DFR and beta_behav[, col]
## t = -0.048345, df = 150, p-value = 0.9615
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1630455 0.1553510
## sample estimates:
## cor
## -0.003947308## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 4 rows containing non-finite values (stat_smooth).## Warning: Removed 4 rows containing missing values (geom_point).
## [1] "delay_midOccip"
##
## Pearson's product-moment correlation
##
## data: beta_behav$omnibus_span_no_DFR and beta_behav[, col]
## t = -0.33429, df = 147, p-value = 0.7386
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1875300 0.1338319
## sample estimates:
## cor
## -0.02756117## `geom_smooth()` using formula 'y ~ x'
## [1] "delay_Oz"
##
## Pearson's product-moment correlation
##
## data: beta_behav$omnibus_span_no_DFR and beta_behav[, col]
## t = -0.61987, df = 151, p-value = 0.5363
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2074003 0.1091707
## sample estimates:
## cor
## -0.05038019## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
## [1] "probe_midOccip"
##
## Pearson's product-moment correlation
##
## data: beta_behav$omnibus_span_no_DFR and beta_behav[, col]
## t = -0.47641, df = 149, p-value = 0.6345
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1974971 0.1214863
## sample estimates:
## cor
## -0.03899893## `geom_smooth()` using formula 'y ~ x'
## [1] "probe_Oz"
##
## Pearson's product-moment correlation
##
## data: beta_behav$omnibus_span_no_DFR and beta_behav[, col]
## t = -1.2112, df = 151, p-value = 0.2277
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2528336 0.0615453
## sample estimates:
## cor
## -0.09809077Split by WMC
There are, however, no differences across groups in beta for either the high load trials or load effects.
midOccip_DFR_beta <- paired_freq_plot(split_betas_plot[["ERSPS_midOccip_DFR"]][["long"]], c("high","low", "load_effect"))
Oz_DFR_beta <- paired_freq_plot(split_betas_plot[["ERSPS_Oz_DFR"]][["long"]], c("high","low", "load_effect"))
midOccip_DFR_beta[[1]] + midOccip_DFR_beta[[2]]+
plot_annotation(title="midOccip - beta during DFR")+
plot_layout(guides="collect")
Oz_DFR_beta[[1]] + Oz_DFR_beta[[2]]+
plot_annotation(title="Oz - beta during DFR")+
plot_layout(guides="collect")
beta_cue_Oz_DFR_split <- split_into_groups(beta_cue_average_Oz, WM_groups)
beta_cue_midOccip_DFR_split <- split_into_groups(beta_cue_average_midOccip, WM_groups)
beta_delay_Oz_DFR_split <- split_into_groups(beta_delay_average_Oz, WM_groups)
beta_delay_midOccip_DFR_split <- split_into_groups(beta_delay_average_midOccip, WM_groups)
beta_probe_Oz_DFR_split <- split_into_groups(beta_probe_average_Oz, WM_groups)
beta_probe_midOccip_DFR_split <- split_into_groups(beta_probe_average_midOccip, WM_groups)
beta_cue_Oz_DFR_split_prepped <- prep_split_for_bar_plots(beta_cue_Oz_DFR_split)
beta_cue_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(beta_cue_midOccip_DFR_split)
beta_delay_Oz_DFR_split_prepped <- prep_split_for_bar_plots(beta_delay_Oz_DFR_split)
beta_delay_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(beta_delay_midOccip_DFR_split)
beta_probe_Oz_DFR_split_prepped <- prep_split_for_bar_plots(beta_probe_Oz_DFR_split)
beta_probe_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(beta_probe_midOccip_DFR_split)beta_cue_midOccip_DFR_anova <- merge(beta_cue_average_midOccip,WM_to_merge, by="PTID")
beta_delay_midOccip_DFR_anova <- merge(beta_delay_average_midOccip,WM_to_merge, by="PTID")
beta_probe_midOccip_DFR_anova <- merge(beta_probe_average_midOccip,WM_to_merge, by="PTID")
beta_cue_Oz_DFR_anova <- merge(beta_cue_average_Oz,WM_to_merge, by="PTID")
beta_delay_Oz_DFR_anova <- merge(beta_delay_average_Oz,WM_to_merge, by="PTID")
beta_probe_Oz_DFR_anova <- merge(beta_probe_average_Oz,WM_to_merge, by="PTID")beta_cue_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_cue_Oz_LE
beta_cue_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_cue_midOccip_LE
beta_delay_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_delay_Oz_LE
beta_delay_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_delay_midOccip_LE
beta_probe_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_probe_Oz_LE
beta_probe_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> beta_probe_midOccip_LE
beta_cue_Oz_LE + beta_delay_Oz_LE + beta_probe_Oz_LE+
plot_annotation(title="Oz electrode beta frequency Load Effect during DFR")
beta_cue_midOccip_LE + beta_delay_midOccip_LE + beta_probe_midOccip_LE+
plot_annotation(title="midOccip cluster beta frequency Load Effect during DFR")
beta_cue_midOccip.aov <- aov(load_effect ~ level, data = beta_cue_midOccip_DFR_anova)
print("beta - cue midOccip")## [1] "beta - cue midOccip"print(summary(beta_cue_midOccip.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.44 0.2184 0.215 0.807
## Residuals 148 150.35 1.0159beta_delay_midOccip.aov <- aov(load_effect ~ level, data = beta_delay_midOccip_DFR_anova)
print("beta - delay midOccip")## [1] "beta - delay midOccip"print(summary(beta_delay_midOccip.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.17 0.0848 0.069 0.933
## Residuals 148 182.06 1.2301beta_probe_midOccip.aov <- aov(load_effect ~ level, data = beta_probe_midOccip_DFR_anova)
print("beta - probe midOccip")## [1] "beta - probe midOccip"print(summary(beta_probe_midOccip.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 3.43 1.715 0.97 0.381
## Residuals 148 261.66 1.768beta_cue_Oz.aov <- aov(load_effect ~ level, data = beta_cue_Oz_DFR_anova)
print("beta - cue Oz")## [1] "beta - cue Oz"print(summary(beta_cue_Oz.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.56 0.7819 0.646 0.526
## Residuals 158 191.22 1.2103beta_delay_Oz.aov <- aov(load_effect ~ level, data = beta_delay_Oz_DFR_anova)
print("beta - delay Oz")## [1] "beta - delay Oz"print(summary(beta_delay_Oz.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.72 1.358 1.236 0.293
## Residuals 158 173.64 1.099Low Gamma
All Subjects
Significant load effects during delay and probe in the mid Occipital cluster and Oz electrode (low > high) and F avg during probe (low > high).
low_gamma_plot_list[["ERSPS_midOccip_DFR"]][["indiv_loads"]] + low_gamma_plot_list[["ERSPS_midOccip_DFR"]][["LE"]] +
plot_annotation(title="midOccip - low_gamma during DFR")
low_gamma_plot_list[["ERSPS_Oz_DFR"]][["indiv_loads"]] + low_gamma_plot_list[["ERSPS_Oz_DFR"]][["LE"]] +
plot_annotation(title="Oz - low_gamma during DFR")
low_gamma_mid_occip_times <- time_sig_load_effects(low_gamma[["ERSPS_midOccip_DFR"]][["load_effect"]], ERSPS_times_DFR)
low_gamma_Oz_times <- time_sig_load_effects(low_gamma[["ERSPS_Oz_DFR"]][["load_effect"]], ERSPS_times_DFR)low_gamma_cue_average_midOccip <- select_period_average(low_gamma[["ERSPS_midOccip_DFR"]],496.09375,742.18750,ERSPS_times_DFR)
low_gamma_cue_average_Oz <- select_period_average(low_gamma[["ERSPS_Oz_DFR"]],1437.50000,2433.59375,ERSPS_times_DFR)
low_gamma_delay_average_midOccip <- select_period_average(low_gamma[["ERSPS_midOccip_DFR"]],2582.03125,3078.12500,ERSPS_times_DFR)
low_gamma_delay_average_Oz <- select_period_average(low_gamma[["ERSPS_Oz_DFR"]],2878.90625,3175.78125,ERSPS_times_DFR)
low_gamma_probe_average_midOccip <- select_period_average(low_gamma[["ERSPS_midOccip_DFR"]],5609.37500,6800.78125,ERSPS_times_DFR)
low_gamma_probe_average_Oz <- select_period_average(low_gamma[["ERSPS_Oz_DFR"]],5660.15625,6652.34375,ERSPS_times_DFR)t.test(low_gamma_cue_average_midOccip$load_effect,mu=0)##
## One Sample t-test
##
## data: low_gamma_cue_average_midOccip$load_effect
## t = 3.0978, df = 177, p-value = 0.002268
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## 0.0411968 0.1858122
## sample estimates:
## mean of x
## 0.1135045t.test(low_gamma_cue_average_Oz$load_effect,mu=0)##
## One Sample t-test
##
## data: low_gamma_cue_average_Oz$load_effect
## t = -2.8706, df = 189, p-value = 0.004564
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.37793883 -0.07007582
## sample estimates:
## mean of x
## -0.2240073t.test(low_gamma_delay_average_midOccip$load_effect,mu=0)##
## One Sample t-test
##
## data: low_gamma_delay_average_midOccip$load_effect
## t = -3.1938, df = 177, p-value = 0.001662
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.31132319 -0.07352651
## sample estimates:
## mean of x
## -0.1924248t.test(low_gamma_delay_average_Oz$load_effect,mu=0)##
## One Sample t-test
##
## data: low_gamma_delay_average_Oz$load_effect
## t = -3.07, df = 189, p-value = 0.002456
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.4840476 -0.1053392
## sample estimates:
## mean of x
## -0.2946934t.test(low_gamma_probe_average_midOccip$load_effect,mu=0)##
## One Sample t-test
##
## data: low_gamma_probe_average_midOccip$load_effect
## t = -4.5266, df = 177, p-value = 1.097e-05
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.6269540 -0.2462602
## sample estimates:
## mean of x
## -0.4366071t.test(low_gamma_probe_average_Oz$load_effect,mu=0)##
## One Sample t-test
##
## data: low_gamma_probe_average_Oz$load_effect
## t = -4.6853, df = 189, p-value = 5.334e-06
## alternative hypothesis: true mean is not equal to 0
## 95 percent confidence interval:
## -0.8101677 -0.3300941
## sample estimates:
## mean of x
## -0.5701309low_gamma_data <- Reduce(function(x,y) merge(x,y, by="PTID"),
list(low_gamma_cue_average_midOccip[,c(1,4)], low_gamma_cue_average_Oz[,c(1,4)],
low_gamma_delay_average_midOccip[,c(1,4)],low_gamma_delay_average_Oz[,c(1,4)],
low_gamma_probe_average_midOccip[,c(1,4)],
low_gamma_probe_average_Oz[,c(1,4)]))## Warning in merge.data.frame(x, y, by = "PTID"): column names 'load_effect.x',
## 'load_effect.y' are duplicated in the result
## Warning in merge.data.frame(x, y, by = "PTID"): column names 'load_effect.x',
## 'load_effect.y' are duplicated in the result## Warning in merge.data.frame(x, y, by = "PTID"): column names 'load_effect.x',
## 'load_effect.y', 'load_effect.x', 'load_effect.y' are duplicated in the resultcolnames(low_gamma_data) <- c("PTID", "cue_midOccip", "cue_Oz", "delay_midOccip","delay_Oz", "probe_midOccip", "probe_Oz")
low_gamma_behav <- merge(ERSP_behav_corr_data,low_gamma_data, by = "PTID")
for (col in seq.int(5,9)){
low_gamma_behav[((low_gamma_behav[,col] > mean(low_gamma_behav[,col])+3*sd(low_gamma_behav[,col])) |
(low_gamma_behav[,col] < mean(low_gamma_behav[,col])-3*sd(low_gamma_behav[,col]))), col] <- NA
}
for (col in seq.int(5,10)){
print(ggplot(data = low_gamma_behav, aes_string(x="omnibus_span_no_DFR", y = colnames(low_gamma_behav)[col]))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1))
print(colnames(low_gamma_behav)[col])
print(cor.test(low_gamma_behav$omnibus_span_no_DFR, low_gamma_behav[,col]))
}## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
## [1] "cue_midOccip"
##
## Pearson's product-moment correlation
##
## data: low_gamma_behav$omnibus_span_no_DFR and low_gamma_behav[, col]
## t = 0.81173, df = 150, p-value = 0.4182
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.09405886 0.22298517
## sample estimates:
## cor
## 0.06613212## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).## Warning: Removed 2 rows containing missing values (geom_point).
## [1] "cue_Oz"
##
## Pearson's product-moment correlation
##
## data: low_gamma_behav$omnibus_span_no_DFR and low_gamma_behav[, col]
## t = -0.6178, df = 149, p-value = 0.5376
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.2085920 0.1100694
## sample estimates:
## cor
## -0.05054771## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).
## [1] "delay_midOccip"
##
## Pearson's product-moment correlation
##
## data: low_gamma_behav$omnibus_span_no_DFR and low_gamma_behav[, col]
## t = -0.3149, df = 149, p-value = 0.7533
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1847563 0.1344936
## sample estimates:
## cor
## -0.02578884## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 2 rows containing non-finite values (stat_smooth).
## Warning: Removed 2 rows containing missing values (geom_point).
## [1] "delay_Oz"
##
## Pearson's product-moment correlation
##
## data: low_gamma_behav$omnibus_span_no_DFR and low_gamma_behav[, col]
## t = 1.028, df = 149, p-value = 0.3056
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.07683779 0.24042633
## sample estimates:
## cor
## 0.08392066## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 1 rows containing non-finite values (stat_smooth).## Warning: Removed 1 rows containing missing values (geom_point).
## [1] "probe_midOccip"
##
## Pearson's product-moment correlation
##
## data: low_gamma_behav$omnibus_span_no_DFR and low_gamma_behav[, col]
## t = 0.70054, df = 150, p-value = 0.4847
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1030322 0.2143571
## sample estimates:
## cor
## 0.05710515## `geom_smooth()` using formula 'y ~ x'
## [1] "probe_Oz"
##
## Pearson's product-moment correlation
##
## data: low_gamma_behav$omnibus_span_no_DFR and low_gamma_behav[, col]
## t = -2.1596, df = 151, p-value = 0.03238
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.32290526 -0.01482531
## sample estimates:
## cor
## -0.1730962Split by WMC
In the low gamma band, the only load effect that shows any difference is Oz during cue, with a trend for an inverted U shape relationship.
In the raw power, however, we do see differences between groups in the cue and delay periods in the mid occipcital cluster and Oz electrodes. In the cue and delay for mid occipital and delay for Oz, we see a significant low > high difference, and in cue period for the mid occipital and cue and delay for Oz, we see a med > high difference.
midOccip_DFR_low_gamma <- paired_freq_plot(split_low_gammas_plot[["ERSPS_midOccip_DFR"]][["long"]], c("high","low", "load_effect"))
Oz_DFR_low_gamma <- paired_freq_plot(split_low_gammas_plot[["ERSPS_Oz_DFR"]][["long"]], c("high","low", "load_effect"))
midOccip_DFR_low_gamma[[1]] + midOccip_DFR_low_gamma[[2]]+
plot_annotation(title="midOccip - low_gamma during DFR")+
plot_layout(guides="collect")
Oz_DFR_low_gamma[[1]] + Oz_DFR_low_gamma[[2]]+
plot_annotation(title="Oz - low_gamma during DFR")+
plot_layout(guides="collect")
low_gamma_cue_Oz_DFR_split <- split_into_groups(low_gamma_cue_average_Oz, WM_groups)
low_gamma_cue_midOccip_DFR_split <- split_into_groups(low_gamma_cue_average_midOccip, WM_groups)
low_gamma_delay_Oz_DFR_split <- split_into_groups(low_gamma_delay_average_Oz, WM_groups)
low_gamma_delay_midOccip_DFR_split <- split_into_groups(low_gamma_delay_average_midOccip, WM_groups)
low_gamma_probe_Oz_DFR_split <- split_into_groups(low_gamma_probe_average_Oz, WM_groups)
low_gamma_probe_midOccip_DFR_split <- split_into_groups(low_gamma_probe_average_midOccip, WM_groups)
low_gamma_cue_Oz_DFR_split_prepped <- prep_split_for_bar_plots(low_gamma_cue_Oz_DFR_split)
low_gamma_cue_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(low_gamma_cue_midOccip_DFR_split)
low_gamma_delay_Oz_DFR_split_prepped <- prep_split_for_bar_plots(low_gamma_delay_Oz_DFR_split)
low_gamma_delay_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(low_gamma_delay_midOccip_DFR_split)
low_gamma_probe_Oz_DFR_split_prepped <- prep_split_for_bar_plots(low_gamma_probe_Oz_DFR_split)
low_gamma_probe_midOccip_DFR_split_prepped <- prep_split_for_bar_plots(low_gamma_probe_midOccip_DFR_split)low_gamma_cue_midOccip_DFR_anova <- merge(low_gamma_cue_average_midOccip,WM_to_merge, by="PTID")
low_gamma_delay_midOccip_DFR_anova <- merge(low_gamma_delay_average_midOccip,WM_to_merge, by="PTID")
low_gamma_probe_midOccip_DFR_anova <- merge(low_gamma_probe_average_midOccip,WM_to_merge, by="PTID")
low_gamma_cue_Oz_DFR_anova <- merge(low_gamma_cue_average_Oz,WM_to_merge, by="PTID")
low_gamma_delay_Oz_DFR_anova <- merge(low_gamma_delay_average_Oz,WM_to_merge, by="PTID")
low_gamma_probe_Oz_DFR_anova <- merge(low_gamma_probe_average_Oz,WM_to_merge, by="PTID")low_gamma_cue_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_cue_Oz_LE
low_gamma_cue_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Cue")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_cue_midOccip_LE
low_gamma_delay_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_delay_Oz_LE
low_gamma_delay_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Delay")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_delay_midOccip_LE
low_gamma_probe_Oz_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_probe_Oz_LE
low_gamma_probe_midOccip_DFR_split_prepped[["melt_data"]] %>%
filter(variable=="load_effect") %>%
mutate(level = factor(level, levels =c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE, ymax=Means+SE), width=0.2)+
ggtitle("Probe")+
xlab("WMC")+
ylab("ERP amplitude LE")+
theme_classic()+
theme(aspect.ratio = 1) -> low_gamma_probe_midOccip_LE
low_gamma_cue_Oz_LE + low_gamma_delay_Oz_LE + low_gamma_probe_Oz_LE+
plot_annotation(title="Oz electrode low_gamma frequency Load Effect during DFR")
low_gamma_cue_midOccip_LE + low_gamma_delay_midOccip_LE + low_gamma_probe_midOccip_LE+
plot_annotation(title="midOccip cluster low_gamma frequency Load Effect during DFR")
low_gamma_cue_midOccip.aov <- aov(load_effect ~ level, data = low_gamma_cue_midOccip_DFR_anova)
print("low_gamma - cue midOccip")## [1] "low_gamma - cue midOccip"print(summary(low_gamma_cue_midOccip.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.01 0.00417 0.018 0.982
## Residuals 148 33.37 0.22550low_gamma_delay_midOccip.aov <- aov(load_effect ~ level, data = low_gamma_delay_midOccip_DFR_anova)
print("low_gamma - delay midOccip")## [1] "low_gamma - delay midOccip"print(summary(low_gamma_delay_midOccip.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.21 0.1033 0.152 0.859
## Residuals 148 100.57 0.6795low_gamma_cue_Oz.aov <- aov(load_effect ~ level, data = low_gamma_cue_Oz_DFR_anova)
print("low_gamma - cue Oz")## [1] "low_gamma - cue Oz"print(summary(low_gamma_cue_Oz.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.66 0.3303 0.294 0.746
## Residuals 158 177.67 1.1245low_gamma_delay_Oz.aov <- aov(load_effect ~ level, data = low_gamma_delay_Oz_DFR_anova)
print("low_gamma - delay Oz")## [1] "low_gamma - delay Oz"print(summary(low_gamma_delay_Oz.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.1 0.0494 0.027 0.973
## Residuals 158 289.7 1.8335CDA
CDA <- data.frame(read.csv("data/EEG/LCD/ERPS_CDA.txt"))
CDA_fMRI <- CDA[CDA$subID %in% constructs_fMRI$PTID,]
CDA_fMRI[is.nan(CDA_fMRI[,2]),2:11] <- NA
colnames(CDA_fMRI)[1] <- "PTID"
CDA_fMRI$CDA_3_1 <- CDA_fMRI$LCD_CDA_3dots - CDA_fMRI$LCD_CDA_1dots
CDA_fMRI$CDA_5_1 <- CDA_fMRI$LCD_CDA_5dots - CDA_fMRI$LCD_CDA_1dots
CDA_fMRI$CDA_5_3 <- CDA_fMRI$LCD_CDA_5dots - CDA_fMRI$LCD_CDA_3dotsRaw Data
First, let’s take a look at the raw data, just for sanity’s sake.
CDA_fMRI %>%
select(c("LCD_CDA_1dots", "LCD_CDA_3dots", "LCD_CDA_5dots")) %>%
melt() %>%
ggplot(aes(x=variable,y=value))+
geom_jitter()+
stat_summary(fun.data=mean_sdl, geom="pointrange", color="red")+
xlab("Level")+
ylab("CDA")## No id variables; using all as measure variables## Warning: Removed 24 rows containing non-finite values (stat_summary).## Warning: Removed 24 rows containing missing values (geom_point).
CDA_fMRI %>%
select(c("CDA_3_1", "CDA_5_1", "CDA_5_3")) %>%
melt() %>%
ggplot(aes(x=variable,y=value))+
geom_jitter()+
stat_summary(fun.data=mean_sdl, geom="pointrange", color="red")+
xlab("Level differences")+
ylab("CDA Load Effects")## No id variables; using all as measure variables## Warning: Removed 24 rows containing non-finite values (stat_summary).
## Warning: Removed 24 rows containing missing values (geom_point).
Relationship of Raw Data with Span
Other studies have shown a relationship between the CDA and capacity as measured by LCD K. Our data, only showed a correlation between the CDA in the low load condition (1 dot) and the LCD K max, with no correlation between omnibus span or DFR load effect.
data_to_plot <- data.frame(CDA_fMRI, p200_delay_DFR, omnibus_span = constructs_fMRI$omnibus_span_no_DFR)
data_to_plot <- merge(data_to_plot,p200_cog)
(ggplot(data_to_plot,aes(x=DFR_Load3_Load1, y=LCD_CDA_5dots))+
geom_point()+
stat_smooth(method="lm")+
theme_classic())+(ggplot(data_to_plot,aes(x=DFR_Load3_Load1, y=LCD_CDA_3dots))+
geom_point()+
stat_smooth(method="lm")+
theme_classic())+(ggplot(data_to_plot,aes(x=DFR_Load3_Load1, y=LCD_CDA_1dots))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 8 rows containing non-finite values (stat_smooth).## Warning: Removed 8 rows containing missing values (geom_point).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
cor.test(data_to_plot$LCD_CDA_5dots, data_to_plot$DFR_Load3_Load1)##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_5dots and data_to_plot$DFR_Load3_Load1
## t = 0.1352, df = 160, p-value = 0.8926
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1437443 0.1646122
## sample estimates:
## cor
## 0.01068802cor.test(data_to_plot$LCD_CDA_3dots, data_to_plot$DFR_Load3_Load1)##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_3dots and data_to_plot$DFR_Load3_Load1
## t = -1.524, df = 160, p-value = 0.1295
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.26885513 0.03522659
## sample estimates:
## cor
## -0.1196186cor.test(data_to_plot$LCD_CDA_1dots, data_to_plot$DFR_Load3_Load1)##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_1dots and data_to_plot$DFR_Load3_Load1
## t = 0.92497, df = 160, p-value = 0.3564
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.08218942 0.22460001
## sample estimates:
## cor
## 0.0729303(ggplot(data_to_plot,aes(x=omnibus_span, y=LCD_CDA_5dots))+
geom_point()+
stat_smooth(method="lm")+
theme_classic())+(ggplot(data_to_plot,aes(x=omnibus_span, y=LCD_CDA_3dots))+
geom_point()+
stat_smooth(method="lm")+
theme_classic())+(ggplot(data_to_plot,aes(x=omnibus_span, y=LCD_CDA_1dots))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
cor.test(data_to_plot$LCD_CDA_5dots, data_to_plot$omnibus_span)##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_5dots and data_to_plot$omnibus_span
## t = -0.81334, df = 160, p-value = 0.4172
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.21622386 0.09092732
## sample estimates:
## cor
## -0.06416781cor.test(data_to_plot$LCD_CDA_3dots, data_to_plot$omnibus_span)##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_3dots and data_to_plot$omnibus_span
## t = -1.4802, df = 160, p-value = 0.1408
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.26566128 0.03866141
## sample estimates:
## cor
## -0.1162269cor.test(data_to_plot$LCD_CDA_1dots, data_to_plot$omnibus_span)##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_1dots and data_to_plot$omnibus_span
## t = -1.3219, df = 160, p-value = 0.1881
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.25406112 0.05107662
## sample estimates:
## cor
## -0.1039374(ggplot(data_to_plot,aes(x=XLCD_K_MAX, y=LCD_CDA_5dots))+
geom_point()+
stat_smooth(method="lm")+
theme_classic())+(ggplot(data_to_plot,aes(x=XLCD_K_MAX, y=LCD_CDA_3dots))+
geom_point()+
stat_smooth(method="lm")+
theme_classic())+(ggplot(data_to_plot,aes(x=XLCD_K_MAX, y=LCD_CDA_1dots))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 9 rows containing non-finite values (stat_smooth).## Warning: Removed 9 rows containing missing values (geom_point).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
cor.test(data_to_plot$LCD_CDA_5dots, data_to_plot$XLCD_K_MAX)##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_5dots and data_to_plot$XLCD_K_MAX
## t = -0.48689, df = 159, p-value = 0.627
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1921126 0.1167875
## sample estimates:
## cor
## -0.03858431cor.test(data_to_plot$LCD_CDA_3dots, data_to_plot$XLCD_K_MAX)##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_3dots and data_to_plot$XLCD_K_MAX
## t = -1.2834, df = 159, p-value = 0.2012
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.25198309 0.05427012
## sample estimates:
## cor
## -0.1012547cor.test(data_to_plot$LCD_CDA_1dots, data_to_plot$XLCD_K_MAX)##
## Pearson's product-moment correlation
##
## data: data_to_plot$LCD_CDA_1dots and data_to_plot$XLCD_K_MAX
## t = -2.0777, df = 159, p-value = 0.03935
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.309470392 -0.008106698
## sample estimates:
## cor
## -0.1625777CDA_split <- split_into_groups(CDA_fMRI, WM_groups)
CDA_split_plot_data <- prep_split_for_bar_plots(CDA_split)CDA_split_plot_data[["melt_data"]] %>%
filter(variable == "LCD_CDA_5dots") %>%
mutate(level = factor(level,levels=c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE,ymax=Means+SE), width=0.2)+
xlab("WMC group")+
ylab("CDA")+
ggtitle("CDA - 5 dots")+
theme_classic()+
theme(aspect.ratio=1)
CDA_split_plot_data[["melt_data"]] %>%
filter(variable == "LCD_CDA_3dots") %>%
mutate(level = factor(level,levels=c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE,ymax=Means+SE), width=0.2)+
xlab("WMC group")+
ylab("CDA")+
ggtitle("CDA - 3 dots")+
theme_classic()+
theme(aspect.ratio=1)
CDA_split_plot_data[["melt_data"]] %>%
filter(variable == "LCD_CDA_1dots") %>%
mutate(level = factor(level,levels=c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE,ymax=Means+SE), width=0.2)+
xlab("WMC group")+
ylab("CDA")+
ggtitle("CDA - 1 dot")+
theme_classic()+
theme(aspect.ratio=1)
CDA_anova <- merge(CDA_fMRI,WM_to_merge, by="PTID")
summary(aov(LCD_CDA_1dots ~ level,data=CDA_anova))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.64 0.3182 1.053 0.351
## Residuals 157 47.44 0.3022
## 8 observations deleted due to missingnesssummary(aov(LCD_CDA_3dots ~ level,data=CDA_anova))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.45 0.7228 1.684 0.189
## Residuals 157 67.37 0.4291
## 8 observations deleted due to missingnessRelationship of Load Effects with Span
Vogel and Machizawa (2004) had mean K = 2.8. Our average K max = 2.68 (so slightly lower). The only thing that even shows a trend with DFR fMRI load effects is in the load effect between the 3 dot and 1 dot condition.
(ggplot(data_to_plot,aes(x=DFR_Load3_Load1, y=CDA_5_3))+
geom_point()+
stat_smooth(method="lm")+
theme_classic())+(ggplot(data_to_plot,aes(x=DFR_Load3_Load1, y=CDA_5_1))+
geom_point()+
stat_smooth(method="lm")+
theme_classic())+(ggplot(data_to_plot,aes(x=DFR_Load3_Load1, y=CDA_3_1))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1))## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 8 rows containing non-finite values (stat_smooth).## Warning: Removed 8 rows containing missing values (geom_point).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
cor.test(data_to_plot$CDA_5_3, data_to_plot$DFR_Load3_Load1)##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_5_3 and data_to_plot$DFR_Load3_Load1
## t = 1.3562, df = 160, p-value = 0.177
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.04838862 0.25658013
## sample estimates:
## cor
## 0.1066022cor.test(data_to_plot$CDA_5_1, data_to_plot$DFR_Load3_Load1)##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_5_1 and data_to_plot$DFR_Load3_Load1
## t = -0.56408, df = 160, p-value = 0.5735
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1973896 0.1104038
## sample estimates:
## cor
## -0.04455009cor.test(data_to_plot$CDA_3_1, data_to_plot$DFR_Load3_Load1)##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_3_1 and data_to_plot$DFR_Load3_Load1
## t = -1.9681, df = 160, p-value = 0.05079
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.3008041900 0.0004669451
## sample estimates:
## cor
## -0.1537396(ggplot(data_to_plot,aes(x=omnibus_span, y=CDA_5_3))+
geom_point()+
stat_smooth(method="lm")+
theme_classic())+(ggplot(data_to_plot,aes(x=omnibus_span, y=CDA_5_1))+
geom_point()+
stat_smooth(method="lm")+
theme_classic())+(ggplot(data_to_plot,aes(x=omnibus_span, y=CDA_3_1))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1)
)## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 8 rows containing non-finite values (stat_smooth).
## Warning: Removed 8 rows containing missing values (geom_point).
cor.test(data_to_plot$CDA_5_3, data_to_plot$omnibus_span)##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_5_3 and data_to_plot$omnibus_span
## t = 0.59677, df = 160, p-value = 0.5515
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1078529 0.1998694
## sample estimates:
## cor
## 0.04712628cor.test(data_to_plot$CDA_5_1, data_to_plot$omnibus_span)##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_5_1 and data_to_plot$omnibus_span
## t = 0.30731, df = 160, p-value = 0.759
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1303964 0.1778170
## sample estimates:
## cor
## 0.02428743cor.test(data_to_plot$CDA_3_1, data_to_plot$omnibus_span)##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_3_1 and data_to_plot$omnibus_span
## t = -0.31616, df = 160, p-value = 0.7523
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1784947 0.1297082
## sample estimates:
## cor
## -0.024987mean(data_to_plot$XLCD_K_MAX,na.rm=TRUE)## [1] 2.686799(ggplot(data_to_plot,aes(x=XLCD_K_MAX, y=CDA_5_3))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1))+(ggplot(data_to_plot,aes(x=XLCD_K_MAX, y=CDA_5_1))+
geom_point()+
stat_smooth(method="loess")+
theme_classic())+(ggplot(data_to_plot,aes(x=XLCD_K_MAX, y=CDA_3_1))+
geom_point()+
stat_smooth(method="loess")+
theme_classic()) ## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 9 rows containing non-finite values (stat_smooth).## Warning: Removed 9 rows containing missing values (geom_point).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 9 rows containing non-finite values (stat_smooth).
## Warning: Removed 9 rows containing missing values (geom_point).
cor.test(data_to_plot$CDA_5_3, data_to_plot$XLCD_K_MAX)##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_5_3 and data_to_plot$XLCD_K_MAX
## t = 0.68698, df = 159, p-value = 0.4931
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1011252 0.2073309
## sample estimates:
## cor
## 0.05440059cor.test(data_to_plot$CDA_5_1, data_to_plot$XLCD_K_MAX)##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_5_1 and data_to_plot$XLCD_K_MAX
## t = 1.118, df = 159, p-value = 0.2652
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.06727587 0.23971818
## sample estimates:
## cor
## 0.08831801cor.test(data_to_plot$CDA_3_1, data_to_plot$XLCD_K_MAX)##
## Pearson's product-moment correlation
##
## data: data_to_plot$CDA_3_1 and data_to_plot$XLCD_K_MAX
## t = 0.37401, df = 159, p-value = 0.7089
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## -0.1256028 0.1834815
## sample estimates:
## cor
## 0.02964807ggplot(data_to_plot,aes(x=XLCD_K_MAX, y=omnibus_span))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()## `geom_smooth()` using formula 'y ~ x'## Warning: Removed 3 rows containing non-finite values (stat_smooth).## Warning: Removed 3 rows containing missing values (geom_point).
cor.test(data_to_plot$omnibus_span, data_to_plot$XLCD_K_MAX)##
## Pearson's product-moment correlation
##
## data: data_to_plot$omnibus_span and data_to_plot$XLCD_K_MAX
## t = 8.8404, df = 165, p-value = 1.384e-15
## alternative hypothesis: true correlation is not equal to 0
## 95 percent confidence interval:
## 0.4541707 0.6618156
## sample estimates:
## cor
## 0.5669319Numerically, it looks like there is an inverted U shaped relationship for the 5 dots - 1 dot and 3 dot - 1 dot, but those relationships are not significant.
CDA_split_plot_data[["melt_data"]] %>%
filter(variable == "CDA_5_3") %>%
mutate(level = factor(level,levels=c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE,ymax=Means+SE), width=0.2)+
xlab("WMC group")+
ylab("CDA Load Effect")+
ggtitle("5 dots - 3 dots")+
theme_classic()+
theme(aspect.ratio=1)
CDA_split_plot_data[["melt_data"]] %>%
filter(variable == "CDA_5_1") %>%
mutate(level = factor(level,levels=c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE,ymax=Means+SE), width=0.2)+
xlab("WMC group")+
ylab("CDA Load Effect")+
ggtitle("5 dots - 1 dot")+
theme_classic()+
theme(aspect.ratio=1)
CDA_split_plot_data[["melt_data"]] %>%
filter(variable == "CDA_3_1") %>%
mutate(level = factor(level,levels=c("low","med","high"))) %>%
ggplot()+
geom_bar(aes(x=level,y=Means),stat="identity")+
geom_errorbar(aes(x=level,ymin=Means-SE,ymax=Means+SE), width=0.2)+
xlab("WMC group")+
ylab("CDA Load Effect")+
ggtitle("3 dots - 1 dot")+
theme_classic()+
theme(aspect.ratio=1)
CDA_5_3.aov <- aov(CDA_5_3 ~ level, data = CDA_anova)
print("CDA - 5 dots - 3 dots")## [1] "CDA - 5 dots - 3 dots"print(summary(CDA_5_3.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 1.41 0.7053 1.112 0.331
## Residuals 157 99.58 0.6343
## 8 observations deleted due to missingnessCDA_5_1.aov <- aov(CDA_5_1 ~ level, data = CDA_anova)
print("CDA - 5 dots - 1 dot")## [1] "CDA - 5 dots - 1 dot"print(summary(CDA_5_1.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 0.53 0.263 0.465 0.629
## Residuals 157 88.86 0.566
## 8 observations deleted due to missingnessCDA_3_1.aov <- aov(CDA_3_1 ~ level, data = CDA_anova)
print("CDA - 3 dots - 1 dot")## [1] "CDA - 3 dots - 1 dot"print(summary(CDA_3_1.aov))## Df Sum Sq Mean Sq F value Pr(>F)
## level 2 2.12 1.0607 1.797 0.169
## Residuals 157 92.68 0.5903
## 8 observations deleted due to missingnessCorrelations between EEG and fMRI
p200_delay_DFR$PTID <- p200_delay_avgs$PTID
fMRI_measures <- Reduce(function(x,y) merge(x,y,by="PTID"),
list(p200_delay_DFR, p200_FFA, p200_cue_avgs))
fMRI_measures$CUE_L1_avg <- rowMeans(cbind(fMRI_measures$R_CUE_L1,fMRI_measures$L_CUE_L1))
fMRI_measures$DELAY_L1_avg <- rowMeans(cbind(fMRI_measures$R_DELAY_L1,fMRI_measures$L_DELAY_L1))
fMRI_measures$PROBE_L1_avg <- rowMeans(cbind(fMRI_measures$R_PROBE_L1,fMRI_measures$L_PROBE_L1))
fMRI_measures$CUE_L3_avg <- rowMeans(cbind(fMRI_measures$R_CUE_L3,fMRI_measures$L_CUE_L3))
fMRI_measures$DELAY_L3_avg <- rowMeans(cbind(fMRI_measures$R_DELAY_L3,fMRI_measures$L_DELAY_L3))
fMRI_measures$PROBE_L3_avg <- rowMeans(cbind(fMRI_measures$R_PROBE_L3,fMRI_measures$L_PROBE_L3))
fMRI_measures$CUE_LE_avg <- rowMeans(cbind(fMRI_measures$R_CUE_LE,fMRI_measures$L_CUE_LE))
fMRI_measures$DELAY_LE_avg <- rowMeans(cbind(fMRI_measures$R_DELAY_LE,fMRI_measures$L_DELAY_LE))
fMRI_measures$PROBE_LE_avg <- rowMeans(cbind(fMRI_measures$R_PROBE_LE,fMRI_measures$L_PROBE_LE))
ERP_measures <- Reduce(function(x,y) merge(x, y, by ="PTID"),
list(cue_average_midOccip_n170, cue_average_P3,
probe_average_midOccip_n170, probe_average_P3))
colnames(ERP_measures) <- c("PTID", "low_load_cue_midOccip_n170", "high_load_cue_midOccip_n170", "load_effect_cue_midOccip_n170", "low_load_cue_P3", "high_load_cue_P3", "load_effect_cue_P3", "low_load_probe_midOccip_n170", "high_load_probe_midOccip_n170", "load_effect_probe_midOccip_n170", "low_load_probe_P3", "high_load_probe_P3", "load_effect_probe_P3")
ERSP_measures_alpha <- Reduce(function(x,y) merge(x,y,by="PTID"),
list(alpha_cue_average_midOccip,
alpha_delay_average_midOccip,
alpha_cue_average_Oz,
alpha_delay_average_Oz,
alpha_probe_average_midOccip,
alpha_probe_average_Oz))
colnames(ERSP_measures_alpha) <- c("PTID","high_alpha_cue_midOccip", "low_alpha_cue_midOccip", "LE_alpha_cue_midOccip", "high_alpha_delay_midOccip", "low_alpha_delay_midOccip", "LE_alpha_delay_midOccip", "high_alpha_cue_Oz", "low_alpha_cue_Oz", "LE_alpha_cue_Oz", "high_alpha_delay_Oz", "low_alpha_delay_Oz", "LE_alpha_delay_Oz", "high_alpha_probe_midOccip", "low_alpha_probe_midOccip", "LE_alpha_probe_midOccip","high_alpha_probe_Oz", "low_alpha_probe_Oz", "LE_alpha_probe_Oz" )
ERSP_measures_beta <- Reduce(function(x,y) merge(x,y,by="PTID"),
list(beta_cue_average_midOccip,
beta_delay_average_midOccip,
beta_cue_average_Oz,
beta_delay_average_Oz,
beta_probe_average_midOccip,
beta_probe_average_Oz))
colnames(ERSP_measures_beta) <- c("PTID","high_beta_cue_midOccip", "low_beta_cue_midOccip", "LE_beta_cue_midOccip", "high_beta_delay_midOccip", "low_beta_delay_midOccip", "LE_beta_delay_midOccip", "high_beta_cue_Oz", "low_beta_cue_Oz", "LE_beta_cue_Oz", "high_beta_delay_Oz", "low_beta_delay_Oz", "LE_beta_delay_Oz", "high_beta_probe_midOccip", "low_beta_probe_midOccip", "LE_beta_probe_midOccip","high_beta_probe_Oz", "low_beta_probe_Oz", "LE_beta_probe_Oz" )
ERSP_measures <- merge(ERSP_measures_alpha, ERSP_measures_beta, by= "PTID")ERPs
ERP_fMRI_plots <- list()
ERP_fMRI <- merge(fMRI_measures, ERP_measures, by= "PTID")
sig_linear_ERP_fMRI <- list()
cumulative_df_ERP <- data.frame(matrix(nrow=1,ncol=4))
colnames(cumulative_df_ERP) <- c("ERP_measure", "fMRI_measure", "corr", "p_val")
plot_count <- 1
sig_plot_count <- 1
for (ERP in c(36:45)){
for (fMRI in c(2:4,23,25:33)){
ERP_fMRI_plots[[plot_count]] <- ggplot(ERP_fMRI, aes_string(y=colnames(ERP_fMRI)[ERP],x=colnames(ERP_fMRI)[fMRI]))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1)
cor_results <- cor.test(ERP_fMRI[,ERP], ERP_fMRI[,fMRI])
if (cor_results$p.value < 0.05){
sig_linear_ERP_fMRI[[sig_plot_count]] <- ERP_fMRI_plots[[plot_count]]
sig_plot_count <- sig_plot_count +1
cumulative_df_ERP <- rbind(cumulative_df_ERP, c(colnames(ERP_fMRI)[ERP], colnames(ERP_fMRI)[fMRI], cor_results$estimate, cor_results$p.value))
}
plot_count <- plot_count+1
}
}ERP vs fMRI
It’s hard to interpret these since none of them are comparing like, but just wanted to leave it here for the record.
cumulative_df_ERP <- cumulative_df_ERP[2:nrow(cumulative_df_ERP),]
cumulative_df_ERP %>%
kable(format = "html", escape = F) %>%
kable_styling("striped", full_width = F) | ERP_measure | fMRI_measure | corr | p_val | |
|---|---|---|---|---|
| 2 | load_effect_cue_midOccip_n170 | CUE_L1_avg | 0.168360585458305 | 0.0374979529251927 |
| 3 | low_load_cue_P3 | CUE_LE_avg | 0.182757533804539 | 0.0237514841131259 |
| 4 | load_effect_cue_P3 | DELAY_L3_avg | 0.209448914546025 | 0.00936647450051395 |
| 5 | high_load_probe_P3 | DFR_Load3_Load1 | -0.17219145504146 | 0.0333082803235829 |
| 6 | high_load_probe_P3 | DELAY_L1_avg | 0.180678759270006 | 0.0254191873879704 |
| 7 | load_effect_probe_P3 | DELAY_L1_avg | 0.186714147764717 | 0.0208357542948427 |
| 8 | load_effect_probe_P3 | CUE_L3_avg | -0.201741313033054 | 0.0123946284661481 |
| 9 | load_effect_probe_P3 | PROBE_L3_avg | -0.183907436137367 | 0.0228699996014812 |
| 10 | load_effect_probe_P3 | CUE_LE_avg | -0.203552413827566 | 0.011614854390257 |
for (idx in seq.int(1,length(sig_linear_ERP_fMRI))){
print(sig_linear_ERP_fMRI[[idx]])
}## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
ERSP vs fMRI
ERSP_fMRI_plots <- list()
ERSP_fMRI <- merge(fMRI_measures, ERSP_measures, by= "PTID")
sig_linear_ERSP_fMRI <- list()
cumulative_df_ERSP <- data.frame(matrix(nrow=1,ncol=4))
colnames(cumulative_df_ERSP) <- c("ERSP_measure", "fMRI_measure", "corr", "p_value")
plot_count <- 1
sig_plot_count <- 1
for (ERSP in seq.int(34,69)){
for (fMRI in c(2:4,23,25:33)){
ERSP_fMRI_plots[[plot_count]] <- ggplot(ERSP_fMRI, aes_string(y=colnames(ERSP_fMRI)[ERSP],x=colnames(ERSP_fMRI)[fMRI]))+
geom_point()+
stat_smooth(method="lm")+
theme_classic()+
theme(aspect.ratio=1)
cor_results <- cor.test(ERSP_fMRI[,ERSP], ERSP_fMRI[,fMRI])
if (cor_results$p.value < 0.05){
sig_linear_ERSP_fMRI[[sig_plot_count]] <- ERSP_fMRI_plots[[plot_count]]
sig_plot_count <- sig_plot_count +1
cumulative_df_ERSP <- rbind(cumulative_df_ERSP, c(colnames(ERSP_fMRI)[ERSP], colnames(ERSP_fMRI)[fMRI], cor_results$estimate, cor_results$p.value))
}
plot_count <- plot_count+1
}
}Linear Correlations
Again, hard to interpret because none of the comparisons are like to like, but putting them here.
cumulative_df_ERSP <- cumulative_df_ERSP[2:nrow(cumulative_df_ERSP),]
cumulative_df_ERSP %>%
kable(format = "html", escape = F) %>%
kable_styling("striped", full_width = F) | ERSP_measure | fMRI_measure | corr | p_value | |
|---|---|---|---|---|
| 2 | high_alpha_cue_Oz | DELAY_LE_avg | -0.195597658214643 | 0.0153920599549651 |
| 3 | low_alpha_cue_Oz | DELAY_LE_avg | -0.160355282891383 | 0.0476977295004719 |
| 4 | LE_alpha_probe_midOccip | CUE_LE_avg | -0.165509017536118 | 0.0408975051746419 |
| 5 | LE_alpha_probe_midOccip | PROBE_LE_avg | -0.172061892191283 | 0.0334432088841289 |
| 6 | high_alpha_probe_Oz | DFR_Load1_Delay | -0.179251747539999 | 0.0266212278140106 |
| 7 | high_alpha_probe_Oz | DFR_Load3_Load1 | 0.233266911731895 | 0.00370987103981297 |
| 8 | high_alpha_probe_Oz | PROBE_L1_avg | 0.195132672522671 | 0.0156427050842079 |
| 9 | low_alpha_probe_Oz | PROBE_L1_avg | 0.174102505040299 | 0.0313710424672385 |
| 10 | low_alpha_probe_Oz | PROBE_L3_avg | 0.184809793530868 | 0.0221981190016223 |
| 11 | high_beta_cue_Oz | DELAY_L1_avg | 0.202390385426209 | 0.0121100450391189 |
| 12 | high_beta_cue_Oz | DELAY_LE_avg | -0.189229324415883 | 0.0191474103106513 |
| 13 | low_beta_cue_Oz | DELAY_LE_avg | -0.226001298924605 | 0.00496965081505567 |
| 14 | LE_beta_cue_Oz | CUE_L1_avg | -0.178065719164808 | 0.0276569492321301 |
| 15 | LE_beta_cue_Oz | CUE_L3_avg | -0.203629591326382 | 0.0115826047756051 |
| 16 | high_beta_delay_Oz | DELAY_L1_avg | 0.194487733682514 | 0.0159962332055423 |
| 17 | high_beta_delay_Oz | CUE_LE_avg | -0.166728058042473 | 0.0394138136897848 |
| 18 | low_beta_delay_Oz | DELAY_L1_avg | 0.172184177159515 | 0.0333158473681164 |
| 19 | low_beta_delay_Oz | DELAY_LE_avg | -0.205860242356547 | 0.010683866692058 |
| 20 | LE_beta_delay_Oz | CUE_L3_avg | -0.162708514030265 | 0.0444841669412535 |
| 21 | LE_beta_delay_Oz | CUE_LE_avg | -0.224067516774703 | 0.00536390601194615 |
| 22 | LE_beta_delay_Oz | PROBE_LE_avg | -0.202270814991249 | 0.0121620353649241 |
| 23 | low_beta_probe_Oz | CUE_L1_avg | 0.164980670469936 | 0.0415549595063139 |
| 24 | low_beta_probe_Oz | PROBE_L1_avg | 0.212815139999981 | 0.00826334091584939 |
| 25 | low_beta_probe_Oz | CUE_L3_avg | 0.181534095278279 | 0.0247212229547039 |
| 26 | low_beta_probe_Oz | PROBE_L3_avg | 0.192985022863488 | 0.0168470462878643 |
for (idx in seq.int(1,length(sig_linear_ERSP_fMRI))){
print(sig_linear_ERSP_fMRI[[idx]])
}## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'